Masthead

Introduction

President Obama, first elected in 2008, is said to be the first presidential candidate to have used modern social media tactics to reach out to voters, which contributed a largescale support from the usually inactive younger voter block. As we progress further into the 21st Century, the way social media impacts many facets of our lives only increases. Today, Facebook boasts 1.4 billion people online every month, and is the second largest website on Earth by traffic. Along with Twitter, Facebook has spread social movements far faster and wider than ever possible, and played a large part in movements such as Occupy Wallstreet, the Arab Spring, the LGBT Rights movement, and most recently Black Lives Matter.

With such an evolution in human behavioral patterns, I wanted to see how the availability of mass spread information affected election outcomes. Specifically, I wanted to see if I could correlate spikes in candidate search popularity with direct gains in polls.

For example, if Donald Trump stated his intention of banning all Muslims from entering the United States until immigration could implement better policies to filter out socially beneficial Muslim immigrants from Islamist fundamentalists, I would expect to see a largescale increase in Donald Trump’s Poll Ratings correlating to his virility on the internet by his voters. On the same note, if Bernie Sander’s voiced his support for the “Black Live Matter” movement by acknowledging the leaders when they interupted him in a Democratic Party Primary event, I would expect a general spike in his poll numbers corresponding to voters sympathetic to his cause over the internet.

Ideally, this would be a longterm study leading up to the 2016 Presidential Election. However, within the timeframe of this project’s due date, I was limited to only pre-election polls.

Polling Data

After looking around, I saw that Huffington Post combined the data sources from numerous polling organizations into one location.

Here are the links (http://elections.huffingtonpost.com/pollster/2016-national-gop-primary)
(http://elections.huffingtonpost.com/pollster/2016-national-democratic-primary)

Importing the Data

Importing the data was relatively easy.

sourcecsv = read.csv("http://elections.huffingtonpost.com/pollster/2016-national-gop-primary.csv", header=TRUE, stringsAsFactors=FALSE)
sourcecsv[sourcecsv == "NA"] <- NA
argDF <- sourcecsv

Now here is a snapshot of the data provided. What the CSV Looks like

You can see a few things that are immediately problematic for comparing the data First off, all the sources are thrown into one object with no meaningful separation. At the same time, each polling agency used their own unique timeframe. (For example, Ipsos/Reuters uses a 4 day timeframe while Quinnipiac uses a 7 day timeframe) My first step was to extract all these different parts, and save them into separate dataframes with a unified date structure.

In order to unify the date structures, I took the first day and the last day from each entry point, and created a range of dates in between the two dates, and then populating the new days with repeated data. At the same time, I proceeded to create a three dimentional array, which represents data from January 1, 2015 to December 31, 2015 (As Rows), Candidates (as Columns), and different polling sources (as the third dimention)

Visualizing the data structure before programming it in R, I came up with something like this: Visualizing what I want

So without further ado, here are the respective code for transforming the data for each party (GOP, and Democratic Party)

Transforming the GOP data

The following code can be found at http://andrewshinsuke.me/cs125/fixgop.r

fixday <- function(argDF) {
    modDF<- data.frame(Start.Date=argDF$Start.Date, End.Date=argDF$End.Date, Trump=argDF$Trump, Carson=argDF$Carson, Rubio=argDF$Rubio, Cruz=argDF$Cruz, Bush=argDF$Bush, Rand.Paul=argDF$Rand.Paul, Christie=argDF$Christie, Fiorina=argDF$Fiorina, Kasich=argDF$Kasich, Huckabee=argDF$Huckabee, Santorum=argDF$Santorum, Graham=argDF$Graham, Pataki=argDF$Pataki, Gilmore=argDF$Gilmore, Jindal=argDF$Jindal, Perry=argDF$Perry, Walker=argDF$Walker, Undecided=argDF$Undecided, stringsAsFactors=FALSE)
    modDF[1:2] <- lapply(modDF[1:2], as.Date)
    returnDF <- setDT(modDF)[, list(dates=seq(Start.Date, End.Date, by = '1 day'),
        Trump=Trump, Carson=Carson, Rubio=Rubio, Cruz=Cruz, Bush=Bush, Rand.Paul=Rand.Paul, Christie=Christie, Fiorina=Fiorina, Kasich=Kasich, Huckabee=Huckabee, Santorum=Santorum, Graham=Graham, Pataki=Pataki, Gilmore=Gilmore, Jindal=Jindal, Perry=Perry, Walker=Walker, Undecided=Undecided),
        by = 1:nrow(modDF)][, nrow:= NULL][]
    return(returnDF[order(dates),])
}



includeNA <- function(argDF) {
    returnDF <- data.frame(stringsAsFactors=FALSE)
    argDF <- as.data.frame(argDF, stringsAsFactors=FALSE)

    dateSeq <- seq(as.Date("2015-01-01"), as.Date("2015-12-31"), by="days")
    count = 1
    for(i in seq_along(dateSeq)){
        if(any(dateSeq[i] %in% argDF$dates)==TRUE){
            returnDF <- rbind(returnDF, argDF[match(dateSeq[i], argDF$dates),])
            colnames(returnDF) <- colnames(argDF) 
        }else{
            naList <- data.frame(dateSeq[i], NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, stringsAsFactors=FALSE)
            colnames(naList) <- colnames(argDF)
            returnDF <- rbind(returnDF, naList)
            count <- count + 1}}
    rownames(returnDF) <- as.character(seq(as.Date("2015-01-01"), as.Date("2015-12-31"), by="days"))
    return(returnDF[1:nrow(returnDF), 2:ncol(returnDF)])
}

allArrayMaker <- function(ipsos, quinnipiac, gravis, yougov, abc, fox, ppp, bloomberg, nbcsm, morningconsult, pubrel, mcclatchy, usc, zogby, nbcwsj, ibdtipp, monmouth, emerson, fairleigh, suffolk, cnn, wpaor, lincolnpark, robertmorris, reasonrupe, wilsonpr) {
    xipsos <- includeNA(ipsos)
    xquinnipiac <- includeNA(quinnipiac)
    xgravis <- includeNA(gravis)
    xyougov <- includeNA(yougov)
    xabc <- includeNA(abc)
    xfox <- includeNA(fox)
    xppp <- includeNA(ppp)
    xbloomberg <- includeNA(bloomberg)
    xnbcsm <- includeNA(nbcsm)
    xmorningconsult <- includeNA(morningconsult)
    xpubrel <- includeNA(pubrel)
    xmcclatchy <- includeNA(mcclatchy)
    xusc <- includeNA(usc)
    xzogby <- includeNA(zogby)
    xnbcwsj <- includeNA(nbcwsj)
    xibdtipp <- includeNA(ibdtipp)
    xmonmouth <- includeNA(monmouth)
    xemerson <- includeNA(emerson)
    xfairleigh <- includeNA(fairleigh)
    xsuffolk <- includeNA(suffolk)
    xcnn <- includeNA(cnn)
    xwpaor <- includeNA(wpaor)
    xlincolnpark <- includeNA(lincolnpark)
    xrobertmorris <- includeNA(robertmorris)
    xreasonrupe <- includeNA(reasonrupe)
    xwilsonpr <- includeNA(wilsonpr)
    returnArray <- abind(xipsos, xquinnipiac, xgravis, xyougov, xabc, xfox, xppp, xbloomberg, xnbcsm, xmorningconsult, xpubrel, xmcclatchy, xusc, xzogby, xnbcwsj, xibdtipp, xmonmouth, xemerson, xfairleigh, xsuffolk, xcnn, xwpaor, xlincolnpark, xrobertmorris, xreasonrupe, xwilsonpr, along=3)
    dimnames(returnArray)[[3]] <- c("Ipsos/Reuters", "Quinnipiac", "Gravis Marketing/One America News", "YouGov/Economist", "ABC/Post", "FOX", "PPP (D)", "Bloomberg/Selzer", "NBC/SurveyMonkey", "Morning Consult", "Public Religion Research Institute", "McClatchy/Marist", "USC/LA Times/SurveyMonkey", "Zogby (Internet)", "NBC/WSJ", "IBD/TIPP", "Monmouth University", "Emerson College Polling Society", "Fairleigh Dickinson", "Suffolk/USA Today", "CNN", "Wilson Perkins Allen Opinion Research (R-Cruz)", "Lincoln Park Strategies (D)", "Robert Morris University", "Reason/Rupe", "Wilson Perkins Allen Opinion Research (R)")
    return(returnArray)
}

sourcecsv = read.csv("http://elections.huffingtonpost.com/pollster/2016-national-gop-primary.csv", header=TRUE, stringsAsFactors=FALSE)
#As you can see, I am direcly linking back to the huffington post csv link, so whenever this code is run, it will be updated with the most recent information.
sourcecsv[sourcecsv == "NA"] <- NA
argDF <- sourcecsv



ipsos <- fixday(argDF[argDF$Pollster == "Ipsos/Reuters",])
quinnipiac <- fixday(argDF[argDF$Pollster == "Quinnipiac",])
gravis <- fixday(argDF[argDF$Pollster == "Gravis Marketing/One America News",])
yougov <- fixday(argDF[argDF$Pollster == "YouGov/Economist",])
abc <- fixday(argDF[argDF$Pollster == "ABC/Post",])
fox <- fixday(argDF[argDF$Pollster == "FOX",])
ppp <- fixday(argDF[argDF$Pollster == "PPP (D)",])
bloomberg <- fixday(argDF[argDF$Pollster == "Bloomberg/Selzer",])
nbcsm <- fixday(argDF[argDF$Pollster == "NBC/SurveyMonkey",])
morningconsult <- fixday(argDF[argDF$Pollster == "Morning Consult",])
pubrel <- fixday(argDF[argDF$Pollster == "Public Religion Research Institute",])
mcclatchy <- fixday(argDF[argDF$Pollster == "McClatchy/Marist",])
usc <- fixday(argDF[argDF$Pollster == "USC/LA Times/SurveyMonkey",])
zogby <- fixday(argDF[argDF$Pollster == "Zogby (Internet)",])
nbcwsj <- fixday(argDF[argDF$Pollster == "NBC/WSJ",])
ibdtipp <- fixday(argDF[argDF$Pollster == "IBD/TIPP",])
monmouth <- fixday(argDF[argDF$Pollster == "Monmouth University",])
emerson <- fixday(argDF[argDF$Pollster == "Emerson College Polling Society",])
fairleigh <- fixday(argDF[argDF$Pollster == "Fairleigh Dickinson",])
suffolk <- fixday(argDF[argDF$Pollster == "Suffolk/USA Today",])
cnn <- fixday(argDF[argDF$Pollster == "CNN",])
wpaor <- fixday(argDF[argDF$Pollster == "Wilson Perkins Allen Opinion Research (R-Cruz)",])
lincolnpark <- fixday(argDF[argDF$Pollster == "Lincoln Park Strategies (D)",])
robertmorris <- fixday(argDF[argDF$Pollster == "Robert Morris University",])
reasonrupe <- fixday(argDF[argDF$Pollster == "Reason/Rupe",])
wilsonpr <- fixday(argDF[argDF$Pollster == "Wilson Perkins Allen Opinion Research (R)",])


allGOP <- allArrayMaker(ipsos, quinnipiac, gravis, yougov, abc, fox, ppp, bloomberg, nbcsm, morningconsult, pubrel, mcclatchy, usc, zogby, nbcwsj, ibdtipp, monmouth, emerson, fairleigh, suffolk, cnn, wpaor, lincolnpark, robertmorris, reasonrupe, wilsonpr)


save(allGOP, file="GOPArray.RData")

For the Democratic Party

Here is the code for the Democratic Party’s Array It can be found at http://andrewshinsuke.me/cs125/fixdem.r

#So the only real difference between this code and that of the fixgop.r file is that there were sources such as Wilson Perkins Allen polling that did not poll for the Democratic party. At the same time, there were alot less candidates for the Democratic party (as is the case much of the time for incumbant parties)


fixday <- function(argDF) {
    modDF<- data.frame(Start.Date=argDF$Start.Date, End.Date=argDF$End.Date, Clinton=argDF$Clinton, Sanders=argDF$Sanders, O.Malley=argDF$O.Malley, Biden=argDF$Biden, Chafee=argDF$Chafee, Lessig=argDF$Lessig, Webb=argDF$Webb, Undecided=argDF$Undecided, stringsAsFactors=FALSE)
    modDF[1:2] <- lapply(modDF[1:2], as.Date)
    returnDF <- setDT(modDF)[, list(dates=seq(Start.Date, End.Date, by = '1 day'),
        Clinton=Clinton, Sanders=Sanders, O.Malley=O.Malley, Biden=Biden, Chafee=Chafee, Lessig, Webb=Webb, Undecided=Undecided),
        by = 1:nrow(modDF)][, nrow:= NULL][]
    return(returnDF[order(dates),])
}


includeNA <- function(argDF) {
    returnDF <- data.frame(stringsAsFactors=FALSE)
    argDF <- as.data.frame(argDF, stringsAsFactors=FALSE)

    dateSeq <- seq(as.Date("2015-01-01"), as.Date("2015-12-31"), by="days")
    count = 1
    for(i in seq_along(dateSeq)){
        if(any(dateSeq[i] %in% argDF$dates)==TRUE){
            returnDF <- rbind(returnDF, argDF[match(dateSeq[i], argDF$dates),])
            colnames(returnDF) <- colnames(argDF) 
        }else{
            naList <- data.frame(dateSeq[i], NA, NA, NA, NA, NA, NA, NA, NA, stringsAsFactors=FALSE)
            colnames(naList) <- colnames(argDF)
            returnDF <- rbind(returnDF, naList)
            count <- count + 1}}
    rownames(returnDF) <- as.character(seq(as.Date("2015-01-01"), as.Date("2015-12-31"), by="days"))
    return(returnDF[1:nrow(returnDF), 2:ncol(returnDF)])
}

allArrayMaker <- function(ipsos, quinnipiac, gravis, yougov, abc, fox, ppp, bloomberg, nbcsm, morningconsult, pubrel, mcclatchy, usc, zogby, nbcwsj, ibdtipp, monmouth, emerson, fairleigh, suffolk, cnn, lincolnpark, robertmorris, reasonrupe) {
    xipsos <- includeNA(ipsos)
    xquinnipiac <- includeNA(quinnipiac)
    xgravis <- includeNA(gravis)
    xyougov <- includeNA(yougov)
    xabc <- includeNA(abc)
    xfox <- includeNA(fox)
    xppp <- includeNA(ppp)
    xbloomberg <- includeNA(bloomberg)
    xnbcsm <- includeNA(nbcsm)
    xmorningconsult <- includeNA(morningconsult)
    xpubrel <- includeNA(pubrel)
    xmcclatchy <- includeNA(mcclatchy)
    xusc <- includeNA(usc)
    xzogby <- includeNA(zogby)
    xnbcwsj <- includeNA(nbcwsj)
    xibdtipp <- includeNA(ibdtipp)
    xmonmouth <- includeNA(monmouth)
    xemerson <- includeNA(emerson)
    xfairleigh <- includeNA(fairleigh)
    xsuffolk <- includeNA(suffolk)
    xcnn <- includeNA(cnn)
    xlincolnpark <- includeNA(lincolnpark)
    xrobertmorris <- includeNA(robertmorris)
    xreasonrupe <- includeNA(reasonrupe)
    returnArray <- abind(xipsos, xquinnipiac, xgravis, xyougov, xabc, xfox, xppp, xbloomberg, xnbcsm, xmorningconsult, xpubrel, xmcclatchy, xusc, xzogby, xnbcwsj, xibdtipp, xmonmouth, xemerson, xfairleigh, xsuffolk, xcnn, xlincolnpark, xrobertmorris, xreasonrupe, along=3)
    dimnames(returnArray)[[3]] <- c("Ipsos/Reuters", "Quinnipiac", "Gravis Marketing/One America News", "YouGov/Economist", "ABC/Post", "FOX", "PPP (D)", "Bloomberg/Selzer", "NBC/SurveyMonkey", "Morning Consult", "Public Religion Research Institute", "McClatchy/Marist", "USC/LA Times/SurveyMonkey", "Zogby (Internet)", "NBC/WSJ", "IBD/TIPP", "Monmouth University", "Emerson College Polling Society", "Fairleigh Dickinson", "Suffolk/USA Today", "CNN", "Lincoln Park Strategies (D)", "Robert Morris University", "Reason/Rupe")
    return(returnArray)
}

sourcecsv = read.csv("http://elections.huffingtonpost.com/pollster/2016-national-democratic-primary.csv", header=TRUE, stringsAsFactors=FALSE)
sourcecsv[sourcecsv == "NA"] <- NA
argDF <- sourcecsv


ipsos <- fixday(argDF[argDF$Pollster == "Ipsos/Reuters",])
quinnipiac <- fixday(argDF[argDF$Pollster == "Quinnipiac",])
gravis <- fixday(argDF[argDF$Pollster == "Gravis Marketing/One America News",])
yougov <- fixday(argDF[argDF$Pollster == "YouGov/Economist",])
abc <- fixday(argDF[argDF$Pollster == "ABC/Post",])
fox <- fixday(argDF[argDF$Pollster == "FOX",])
ppp <- fixday(argDF[argDF$Pollster == "PPP (D)",])
bloomberg <- fixday(argDF[argDF$Pollster == "Bloomberg/Selzer",])
nbcsm <- fixday(argDF[argDF$Pollster == "NBC/SurveyMonkey",])
morningconsult <- fixday(argDF[argDF$Pollster == "Morning Consult",])
pubrel <- fixday(argDF[argDF$Pollster == "Public Religion Research Institute",])
mcclatchy <- fixday(argDF[argDF$Pollster == "McClatchy/Marist",])
usc <- fixday(argDF[argDF$Pollster == "USC/LA Times/SurveyMonkey",])
zogby <- fixday(argDF[argDF$Pollster == "Zogby (Internet)",])
nbcwsj <- fixday(argDF[argDF$Pollster == "NBC/WSJ",])
ibdtipp <- fixday(argDF[argDF$Pollster == "IBD/TIPP",])
monmouth <- fixday(argDF[argDF$Pollster == "Monmouth University",])
emerson <- fixday(argDF[argDF$Pollster == "Emerson College Polling Society",])
fairleigh <- fixday(argDF[argDF$Pollster == "Fairleigh Dickinson",])
suffolk <- fixday(argDF[argDF$Pollster == "Suffolk/USA Today",])
cnn <- fixday(argDF[argDF$Pollster == "CNN",])
lincolnpark <- fixday(argDF[argDF$Pollster == "Lincoln Park Strategies (D)",])
robertmorris <- fixday(argDF[argDF$Pollster == "Robert Morris University",])
reasonrupe <- fixday(argDF[argDF$Pollster == "Reason/Rupe",])



allDEM <- allArrayMaker(ipsos, quinnipiac, gravis, yougov, abc, fox, ppp, bloomberg, nbcsm, morningconsult, pubrel, mcclatchy, usc, zogby, nbcwsj, ibdtipp, monmouth, emerson, fairleigh, suffolk, cnn, lincolnpark, robertmorris, reasonrupe)


save(allDEM, file="DEMArray.RData")

Thinking about what to do with the data

So now I created 2 Array’s with all the data I needed, in a unified form to compare with other forms or sources I might need in the future. With all these sources, I thought of the best way to find the “most accurate” picture of all the polling data, and concluded that creating a single data frame with candidates and dates, and taking the mean all the sources would be the most accurate way I could represent this data.

However, even with this organized form, in order to plot this in ggplot2 (which is designed to work with data that is organized so that each row is a different observation of an occurance, or thing, and different columns represent the differnt categories of data), whereas, my data was organized by different dates for rows, and the candidates for columns, I needed to melt the data to have the data points, a date column as a factor, a candidate column as a factor, so that I could graph it in ggplot2

The following code can be found at: http://andrewshinsuke.me/cs125/source.r

# Melting the dataframe using gather from the tidyr package
averagedGOPPoll <- as.data.frame(aaply(allGOP, 1:2, mean, na.rm=TRUE))
averagedGOPPoll$date = as.Date(rownames(averagedGOPPoll))
averagedGOPPoll.melt <- gather(averagedGOPPoll,candidate,percentage,-date)

averagedDEMPoll <- as.data.frame(aaply(allDEM, 1:2, mean, na.rm=TRUE))
averagedDEMPoll$date = as.Date(rownames(averagedDEMPoll))
averagedDEMPoll.melt <- gather(averagedDEMPoll,candidate,percentage,-date)

Here is where I graph the melted data frames.

plotaverageGOPPoll <- ggplot(averagedGOPPoll.melt,aes(x=date,y=percentage,colour=candidate))+
  geom_line() + ylab("Percentage out of 100") + xlab("Dates: January-December 2015") + scale_x_date(labels = date_format("%m/%d"), breaks = date_breaks("month")) + labs(title = "Plot of Averaged Polling Data for the Republicans")


ggsave(plotaverageGOPPoll, file="plotaverageGOPPoll.png")
## Saving 7 x 5 in image
## Warning: Removed 812 rows containing missing values (geom_path).
plotaverageDEMPoll <- ggplot(averagedDEMPoll.melt,aes(x=date,y=percentage,colour=candidate))+
  geom_line() + ylab("Percentage out of 100") + xlab("Dates: January-December 2015") + scale_x_date(labels = date_format("%m/%d"), breaks = date_breaks("month")) + labs(title = "Plot of Averaged Polling Data for the Democrats")

ggsave(plotaverageGOPPoll, file="plotaverageDEMPoll.png")
## Saving 7 x 5 in image
## Warning: Removed 812 rows containing missing values (geom_path).

Visualizing the GOP Averaged Polls looks like this: GOP Poll

Visualizing the Democratic Polls looks like this: DEM Poll

Taking a step back to think about the data

Up to this point, I was able to create two separate data frames with the averaged data from multiple polling sources. Now it was time to use this data to find correlation with the data extracted from the gtrendsR package.

There were a few tricky things about this process. First off, Google Trends only supports querries up to five at a time. Therefore, in order to find all the data I needed, I would need to combine multiple gtrendsr querries into one data frame. Secondly, the data represented is the quantity relative to the total amount of searches for that day, whereas the data represented in the polling data is the percentage of votes each candidate has, (within the party)

This combined with my own lack of formal training in Statistics, led me to conclude that I do not have the resources to undertake a complete analysis project that would lead to any meaningful conclusions about the ability of candidates to tie themselves to a popular sentiment or popular cause.

Instead, I took took it upon myself to find a way to graph the 2 souces of data from Google Trends and the Huffington Post Aggregate data set.

Inspired by Hans Rosling’s Bubble Chart Presentation about world development in his presentation from 2007, I though it would be a great project to try to use a similar graph system to represent the internet interest and polling numbers of each candidate.

The first step was to reform my data so that I would have one data frame per party that combined the elements of both the melted pres_trendDF data frame and the averegedPoll.melt, for each party.

Reformatting the Data Frame in Bubble form for the GOP

I first started by creating a combined data frame called goppolls.

Now one of the problems for trying to create a motion graph is that there cannot be any NaN points in the data because that would make the graph dissapear. Therefore, I had to create an algorithm that replaced NA values of the Poll percentages (the Google Trends data did not come with any non-numeric values).

I did this by checking each element of the Percentages column of the goppolls to check for NA. If the value was not an NA, the value would not change, if it was an NA, then the algorithm would check if the previous number was an NA. If the previous number was not an NA, the new value would become that value. If the previous value was an NA, then, the algorithm would proceed to go through the Percentages column for the closest numeric value, and fill that in for that element.

Next, I needed to create a new column that recorded the change in percentages from one day to the next. This algorithm just went through the Percentages column and subtracted Percentage[i] from Percentage[i-1].

The following code can be found at: http://andrewshinsuke.me/cs125/makeGOPBubble.r

load(url("http://andrewshinsuke.me/cs125/avgGOP.RData"))
load(url("http://andrewshinsuke.me/cs125/pres_trendDF.melt.RData"))



gopgtrends <- pres_trendDF.melt[pres_trendDF.melt$candidate=='Trump' | pres_trendDF.melt$candidate=='Carson' | pres_trendDF.melt$candidate=='Rand',]
goppolls <- averagedGOPPoll.melt[averagedGOPPoll.melt$candidate=='Trump' | averagedGOPPoll.melt$candidate=='Carson' | averagedGOPPoll.melt$candidate=='Rand.Paul',]
gopbubbleDF <- data.frame(Date=gopgtrends$date, Candidate=gopgtrends$candidate, Searched=gopgtrends$searched, Percentage=goppolls$percentage)
#adding a polling percentage column

gopbubbleDF$Percentage[1] <- 0
#Fixing the Percentage data so that there are no more NA's
for(i in seq_along(gopbubbleDF$Percentage[1:length(gopbubbleDF$Percentage)])){
    if(i > 1){
        if(is.nan(gopbubbleDF$Percentage[i])){
            if(!is.nan(gopbubbleDF$Percentage[i-1])){
                    z <- gopbubbleDF$Percentage[i-1]
            }else{
                for(n in gopbubbleDF$Percentage[i:length(gopbubbleDF$Percentage)]){
                    if(!is.na(n)) {
                        z <- n
                        break
                    }
                }
            }
            gopbubbleDF$Percentage[i] <- z
        }
    }
}

changeInPercentage <- c(0)
for(i in seq_along(gopbubbleDF$Percentage[1:length(gopbubbleDF$Percentage)])){
    if(i > 1){
        z <- gopbubbleDF$Percentage[i] - gopbubbleDF$Percentage[i-1]
        changeInPercentage <- append(changeInPercentage, z)}
}

gopbubbleDF <- cbind(gopbubbleDF, Change.In.Polling.Percentage=changeInPercentage)

save(gopbubbleDF, file="gopbubbledf.RData")

Reformatting the Data Frame in Bubble form for the Democratic Party

The process for the democratic party was basically exactly the same source code is at: http://andrewshinsuke.me/cs125/makeDEMBubble.r

load(url("http://andrewshinsuke.me/cs125/avgDEM.RData"))
load(url("http://andrewshinsuke.me/cs125/pres_trendDF.melt.RData"))


averagedDEMPoll.melt
##            date candidate percentage
## 1    2015-01-01   Clinton        NaN
## 2    2015-01-02   Clinton        NaN
## 3    2015-01-03   Clinton        NaN
## 4    2015-01-04   Clinton        NaN
## 5    2015-01-05   Clinton        NaN
## 6    2015-01-06   Clinton        NaN
## 7    2015-01-07   Clinton        NaN
## 8    2015-01-08   Clinton        NaN
## 9    2015-01-09   Clinton        NaN
## 10   2015-01-10   Clinton 61.0000000
## 11   2015-01-11   Clinton 61.0000000
## 12   2015-01-12   Clinton 61.0000000
## 13   2015-01-13   Clinton        NaN
## 14   2015-01-14   Clinton        NaN
## 15   2015-01-15   Clinton        NaN
## 16   2015-01-16   Clinton        NaN
## 17   2015-01-17   Clinton        NaN
## 18   2015-01-18   Clinton        NaN
## 19   2015-01-19   Clinton        NaN
## 20   2015-01-20   Clinton        NaN
## 21   2015-01-21   Clinton        NaN
## 22   2015-01-22   Clinton 60.0000000
## 23   2015-01-23   Clinton 60.0000000
## 24   2015-01-24   Clinton 60.0000000
## 25   2015-01-25   Clinton 57.5000000
## 26   2015-01-26   Clinton 55.0000000
## 27   2015-01-27   Clinton 55.0000000
## 28   2015-01-28   Clinton        NaN
## 29   2015-01-29   Clinton        NaN
## 30   2015-01-30   Clinton        NaN
## 31   2015-01-31   Clinton        NaN
## 32   2015-02-01   Clinton        NaN
## 33   2015-02-02   Clinton        NaN
## 34   2015-02-03   Clinton        NaN
## 35   2015-02-04   Clinton        NaN
## 36   2015-02-05   Clinton        NaN
## 37   2015-02-06   Clinton        NaN
## 38   2015-02-07   Clinton        NaN
## 39   2015-02-08   Clinton        NaN
## 40   2015-02-09   Clinton        NaN
## 41   2015-02-10   Clinton        NaN
## 42   2015-02-11   Clinton        NaN
## 43   2015-02-12   Clinton 61.0000000
## 44   2015-02-13   Clinton 61.0000000
## 45   2015-02-14   Clinton 61.0000000
## 46   2015-02-15   Clinton 61.0000000
## 47   2015-02-16   Clinton        NaN
## 48   2015-02-17   Clinton        NaN
## 49   2015-02-18   Clinton        NaN
## 50   2015-02-19   Clinton        NaN
## 51   2015-02-20   Clinton 54.0000000
## 52   2015-02-21   Clinton 61.0000000
## 53   2015-02-22   Clinton 61.0000000
## 54   2015-02-23   Clinton 68.0000000
## 55   2015-02-24   Clinton        NaN
## 56   2015-02-25   Clinton        NaN
## 57   2015-02-26   Clinton 56.0000000
## 58   2015-02-27   Clinton 56.0000000
## 59   2015-02-28   Clinton 56.0000000
## 60   2015-03-01   Clinton 58.0000000
## 61   2015-03-02   Clinton 58.0000000
## 62   2015-03-03   Clinton 60.0000000
## 63   2015-03-04   Clinton 60.0000000
## 64   2015-03-05   Clinton        NaN
## 65   2015-03-06   Clinton        NaN
## 66   2015-03-07   Clinton 59.0000000
## 67   2015-03-08   Clinton 59.0000000
## 68   2015-03-09   Clinton 59.0000000
## 69   2015-03-10   Clinton        NaN
## 70   2015-03-11   Clinton        NaN
## 71   2015-03-12   Clinton        NaN
## 72   2015-03-13   Clinton 62.0000000
## 73   2015-03-14   Clinton 61.5000000
## 74   2015-03-15   Clinton 61.5000000
## 75   2015-03-16   Clinton 61.0000000
## 76   2015-03-17   Clinton        NaN
## 77   2015-03-18   Clinton        NaN
## 78   2015-03-19   Clinton        NaN
## 79   2015-03-20   Clinton        NaN
## 80   2015-03-21   Clinton 64.0000000
## 81   2015-03-22   Clinton 64.0000000
## 82   2015-03-23   Clinton 64.0000000
## 83   2015-03-24   Clinton        NaN
## 84   2015-03-25   Clinton        NaN
## 85   2015-03-26   Clinton 60.0000000
## 86   2015-03-27   Clinton 60.0000000
## 87   2015-03-28   Clinton 60.0000000
## 88   2015-03-29   Clinton 60.3333333
## 89   2015-03-30   Clinton 58.3333333
## 90   2015-03-31   Clinton 58.3333333
## 91   2015-04-01   Clinton 60.0000000
## 92   2015-04-02   Clinton 60.0000000
## 93   2015-04-03   Clinton        NaN
## 94   2015-04-04   Clinton        NaN
## 95   2015-04-05   Clinton        NaN
## 96   2015-04-06   Clinton        NaN
## 97   2015-04-07   Clinton        NaN
## 98   2015-04-08   Clinton        NaN
## 99   2015-04-09   Clinton        NaN
## 100  2015-04-10   Clinton        NaN
## 101  2015-04-11   Clinton 71.0000000
## 102  2015-04-12   Clinton 71.0000000
## 103  2015-04-13   Clinton 71.0000000
## 104  2015-04-14   Clinton        NaN
## 105  2015-04-15   Clinton        NaN
## 106  2015-04-16   Clinton 64.5000000
## 107  2015-04-17   Clinton 64.5000000
## 108  2015-04-18   Clinton 64.6666667
## 109  2015-04-19   Clinton 64.0000000
## 110  2015-04-20   Clinton 62.3333333
## 111  2015-04-21   Clinton 61.0000000
## 112  2015-04-22   Clinton        NaN
## 113  2015-04-23   Clinton        NaN
## 114  2015-04-24   Clinton        NaN
## 115  2015-04-25   Clinton 59.0000000
## 116  2015-04-26   Clinton 59.0000000
## 117  2015-04-27   Clinton 59.0000000
## 118  2015-04-28   Clinton        NaN
## 119  2015-04-29   Clinton        NaN
## 120  2015-04-30   Clinton        NaN
## 121  2015-05-01   Clinton        NaN
## 122  2015-05-02   Clinton 58.0000000
## 123  2015-05-03   Clinton 58.0000000
## 124  2015-05-04   Clinton 58.0000000
## 125  2015-05-05   Clinton        NaN
## 126  2015-05-06   Clinton        NaN
## 127  2015-05-07   Clinton 63.0000000
## 128  2015-05-08   Clinton 59.5000000
## 129  2015-05-09   Clinton 61.5000000
## 130  2015-05-10   Clinton 61.5000000
## 131  2015-05-11   Clinton 61.0000000
## 132  2015-05-12   Clinton 59.5000000
## 133  2015-05-13   Clinton 56.0000000
## 134  2015-05-14   Clinton 56.0000000
## 135  2015-05-15   Clinton 56.0000000
## 136  2015-05-16   Clinton 58.0000000
## 137  2015-05-17   Clinton 60.0000000
## 138  2015-05-18   Clinton 60.0000000
## 139  2015-05-19   Clinton 57.0000000
## 140  2015-05-20   Clinton 57.0000000
## 141  2015-05-21   Clinton 57.0000000
## 142  2015-05-22   Clinton 57.0000000
## 143  2015-05-23   Clinton 58.0000000
## 144  2015-05-24   Clinton 58.0000000
## 145  2015-05-25   Clinton 58.0000000
## 146  2015-05-26   Clinton 57.0000000
## 147  2015-05-27   Clinton        NaN
## 148  2015-05-28   Clinton 62.0000000
## 149  2015-05-29   Clinton 61.0000000
## 150  2015-05-30   Clinton 61.0000000
## 151  2015-05-31   Clinton 60.0000000
## 152  2015-06-01   Clinton 59.0000000
## 153  2015-06-02   Clinton 57.0000000
## 154  2015-06-03   Clinton        NaN
## 155  2015-06-04   Clinton        NaN
## 156  2015-06-05   Clinton        NaN
## 157  2015-06-06   Clinton 57.0000000
## 158  2015-06-07   Clinton 57.0000000
## 159  2015-06-08   Clinton 57.0000000
## 160  2015-06-09   Clinton        NaN
## 161  2015-06-10   Clinton        NaN
## 162  2015-06-11   Clinton 61.0000000
## 163  2015-06-12   Clinton 61.0000000
## 164  2015-06-13   Clinton 61.3333333
## 165  2015-06-14   Clinton 64.7500000
## 166  2015-06-15   Clinton 68.5000000
## 167  2015-06-16   Clinton 75.0000000
## 168  2015-06-17   Clinton 75.0000000
## 169  2015-06-18   Clinton 75.0000000
## 170  2015-06-19   Clinton        NaN
## 171  2015-06-20   Clinton 55.0000000
## 172  2015-06-21   Clinton 58.0000000
## 173  2015-06-22   Clinton 58.0000000
## 174  2015-06-23   Clinton 61.0000000
## 175  2015-06-24   Clinton        NaN
## 176  2015-06-25   Clinton        NaN
## 177  2015-06-26   Clinton 58.0000000
## 178  2015-06-27   Clinton 58.5000000
## 179  2015-06-28   Clinton 58.5000000
## 180  2015-06-29   Clinton 59.0000000
## 181  2015-06-30   Clinton        NaN
## 182  2015-07-01   Clinton        NaN
## 183  2015-07-02   Clinton        NaN
## 184  2015-07-03   Clinton        NaN
## 185  2015-07-04   Clinton 55.0000000
## 186  2015-07-05   Clinton 55.0000000
## 187  2015-07-06   Clinton 55.0000000
## 188  2015-07-07   Clinton        NaN
## 189  2015-07-08   Clinton        NaN
## 190  2015-07-09   Clinton 57.6666667
## 191  2015-07-10   Clinton 57.6666667
## 192  2015-07-11   Clinton 57.6666667
## 193  2015-07-12   Clinton 57.6666667
## 194  2015-07-13   Clinton 61.0000000
## 195  2015-07-14   Clinton 61.0000000
## 196  2015-07-15   Clinton 59.0000000
## 197  2015-07-16   Clinton 63.0000000
## 198  2015-07-17   Clinton 57.5000000
## 199  2015-07-18   Clinton 57.0000000
## 200  2015-07-19   Clinton 57.0000000
## 201  2015-07-20   Clinton 55.0000000
## 202  2015-07-21   Clinton 57.0000000
## 203  2015-07-22   Clinton 56.0000000
## 204  2015-07-23   Clinton 55.5000000
## 205  2015-07-24   Clinton 55.5000000
## 206  2015-07-25   Clinton 56.3333333
## 207  2015-07-26   Clinton 56.5000000
## 208  2015-07-27   Clinton 56.5000000
## 209  2015-07-28   Clinton 56.5000000
## 210  2015-07-29   Clinton 57.3333333
## 211  2015-07-30   Clinton 54.0000000
## 212  2015-07-31   Clinton 53.5000000
## 213  2015-08-01   Clinton 53.2000000
## 214  2015-08-02   Clinton 53.2000000
## 215  2015-08-03   Clinton 54.3333333
## 216  2015-08-04   Clinton 51.5000000
## 217  2015-08-05   Clinton 52.0000000
## 218  2015-08-06   Clinton        NaN
## 219  2015-08-07   Clinton 56.0000000
## 220  2015-08-08   Clinton 55.5000000
## 221  2015-08-09   Clinton 55.5000000
## 222  2015-08-10   Clinton 55.0000000
## 223  2015-08-11   Clinton 52.0000000
## 224  2015-08-12   Clinton 52.0000000
## 225  2015-08-13   Clinton 48.0000000
## 226  2015-08-14   Clinton 48.0000000
## 227  2015-08-15   Clinton 47.7500000
## 228  2015-08-16   Clinton 47.7500000
## 229  2015-08-17   Clinton 47.0000000
## 230  2015-08-18   Clinton 47.0000000
## 231  2015-08-19   Clinton 47.0000000
## 232  2015-08-20   Clinton 45.0000000
## 233  2015-08-21   Clinton 49.5000000
## 234  2015-08-22   Clinton 49.0000000
## 235  2015-08-23   Clinton 46.5000000
## 236  2015-08-24   Clinton 46.5000000
## 237  2015-08-25   Clinton 46.5000000
## 238  2015-08-26   Clinton 48.0000000
## 239  2015-08-27   Clinton        NaN
## 240  2015-08-28   Clinton 50.3333333
## 241  2015-08-29   Clinton 48.7500000
## 242  2015-08-30   Clinton 48.7500000
## 243  2015-08-31   Clinton 43.3333333
## 244  2015-09-01   Clinton 43.3333333
## 245  2015-09-02   Clinton 43.0000000
## 246  2015-09-03   Clinton        NaN
## 247  2015-09-04   Clinton 44.0000000
## 248  2015-09-05   Clinton 44.5000000
## 249  2015-09-06   Clinton 44.5000000
## 250  2015-09-07   Clinton 44.0000000
## 251  2015-09-08   Clinton 42.2500000
## 252  2015-09-09   Clinton 42.0000000
## 253  2015-09-10   Clinton 42.0000000
## 254  2015-09-11   Clinton 51.3333333
## 255  2015-09-12   Clinton 50.0000000
## 256  2015-09-13   Clinton 50.0000000
## 257  2015-09-14   Clinton 48.6666667
## 258  2015-09-15   Clinton 48.6666667
## 259  2015-09-16   Clinton 47.3333333
## 260  2015-09-17   Clinton 45.2500000
## 261  2015-09-18   Clinton 45.0000000
## 262  2015-09-19   Clinton 44.8571429
## 263  2015-09-20   Clinton 43.7142857
## 264  2015-09-21   Clinton 42.8333333
## 265  2015-09-22   Clinton 45.2500000
## 266  2015-09-23   Clinton 45.6666667
## 267  2015-09-24   Clinton 45.5000000
## 268  2015-09-25   Clinton 47.0000000
## 269  2015-09-26   Clinton 46.0000000
## 270  2015-09-27   Clinton 46.0000000
## 271  2015-09-28   Clinton 46.4000000
## 272  2015-09-29   Clinton 47.7500000
## 273  2015-09-30   Clinton 46.7500000
## 274  2015-10-01   Clinton 45.6000000
## 275  2015-10-02   Clinton 47.5000000
## 276  2015-10-03   Clinton 46.8000000
## 277  2015-10-04   Clinton 46.8000000
## 278  2015-10-05   Clinton 45.6666667
## 279  2015-10-06   Clinton 44.0000000
## 280  2015-10-07   Clinton 44.0000000
## 281  2015-10-08   Clinton 51.0000000
## 282  2015-10-09   Clinton 51.0000000
## 283  2015-10-10   Clinton 49.5000000
## 284  2015-10-11   Clinton 49.5000000
## 285  2015-10-12   Clinton 49.5000000
## 286  2015-10-13   Clinton 48.0000000
## 287  2015-10-14   Clinton 47.0000000
## 288  2015-10-15   Clinton 49.5000000
## 289  2015-10-16   Clinton 53.3333333
## 290  2015-10-17   Clinton 52.1428571
## 291  2015-10-18   Clinton 50.4000000
## 292  2015-10-19   Clinton 50.5000000
## 293  2015-10-20   Clinton 45.0000000
## 294  2015-10-21   Clinton 45.0000000
## 295  2015-10-22   Clinton 53.0000000
## 296  2015-10-23   Clinton 57.0000000
## 297  2015-10-24   Clinton 53.7500000
## 298  2015-10-25   Clinton 55.4000000
## 299  2015-10-26   Clinton 56.4000000
## 300  2015-10-27   Clinton 54.8000000
## 301  2015-10-28   Clinton 53.2500000
## 302  2015-10-29   Clinton 53.4285714
## 303  2015-10-30   Clinton 53.5000000
## 304  2015-10-31   Clinton 54.2000000
## 305  2015-11-01   Clinton 54.5000000
## 306  2015-11-02   Clinton 54.2000000
## 307  2015-11-03   Clinton 54.5000000
## 308  2015-11-04   Clinton 57.0000000
## 309  2015-11-05   Clinton 56.5000000
## 310  2015-11-06   Clinton 58.3333333
## 311  2015-11-07   Clinton 56.5000000
## 312  2015-11-08   Clinton 56.5000000
## 313  2015-11-09   Clinton 57.3333333
## 314  2015-11-10   Clinton 56.5000000
## 315  2015-11-11   Clinton 51.0000000
## 316  2015-11-12   Clinton        NaN
## 317  2015-11-13   Clinton 57.0000000
## 318  2015-11-14   Clinton 54.5000000
## 319  2015-11-15   Clinton 53.2500000
## 320  2015-11-16   Clinton 55.2857143
## 321  2015-11-17   Clinton 55.0000000
## 322  2015-11-18   Clinton 55.6666667
## 323  2015-11-19   Clinton 56.3333333
## 324  2015-11-20   Clinton 54.0000000
## 325  2015-11-21   Clinton 56.0000000
## 326  2015-11-22   Clinton 56.0000000
## 327  2015-11-23   Clinton 57.7500000
## 328  2015-11-24   Clinton 59.0000000
## 329  2015-11-25   Clinton 59.0000000
## 330  2015-11-26   Clinton 60.0000000
## 331  2015-11-27   Clinton 59.0000000
## 332  2015-11-28   Clinton 56.3333333
## 333  2015-11-29   Clinton 56.3333333
## 334  2015-11-30   Clinton 55.0000000
## 335  2015-12-01   Clinton 53.3333333
## 336  2015-12-02   Clinton 52.5000000
## 337  2015-12-03   Clinton 52.7500000
## 338  2015-12-04   Clinton 53.4000000
## 339  2015-12-05   Clinton 54.4000000
## 340  2015-12-06   Clinton 54.6666667
## 341  2015-12-07   Clinton 56.4000000
## 342  2015-12-08   Clinton 57.5000000
## 343  2015-12-09   Clinton 56.0000000
## 344  2015-12-10   Clinton 59.0000000
## 345  2015-12-11   Clinton 56.6666667
## 346  2015-12-12   Clinton 57.0000000
## 347  2015-12-13   Clinton 57.0000000
## 348  2015-12-14   Clinton 55.0000000
## 349  2015-12-15   Clinton 55.0000000
## 350  2015-12-16   Clinton 56.6666667
## 351  2015-12-17   Clinton 56.0000000
## 352  2015-12-18   Clinton        NaN
## 353  2015-12-19   Clinton        NaN
## 354  2015-12-20   Clinton        NaN
## 355  2015-12-21   Clinton        NaN
## 356  2015-12-22   Clinton        NaN
## 357  2015-12-23   Clinton        NaN
## 358  2015-12-24   Clinton        NaN
## 359  2015-12-25   Clinton        NaN
## 360  2015-12-26   Clinton        NaN
## 361  2015-12-27   Clinton        NaN
## 362  2015-12-28   Clinton        NaN
## 363  2015-12-29   Clinton        NaN
## 364  2015-12-30   Clinton        NaN
## 365  2015-12-31   Clinton        NaN
## 366  2015-01-01   Sanders        NaN
## 367  2015-01-02   Sanders        NaN
## 368  2015-01-03   Sanders        NaN
## 369  2015-01-04   Sanders        NaN
## 370  2015-01-05   Sanders        NaN
## 371  2015-01-06   Sanders        NaN
## 372  2015-01-07   Sanders        NaN
## 373  2015-01-08   Sanders        NaN
## 374  2015-01-09   Sanders        NaN
## 375  2015-01-10   Sanders  3.0000000
## 376  2015-01-11   Sanders  3.0000000
## 377  2015-01-12   Sanders  3.0000000
## 378  2015-01-13   Sanders        NaN
## 379  2015-01-14   Sanders        NaN
## 380  2015-01-15   Sanders        NaN
## 381  2015-01-16   Sanders        NaN
## 382  2015-01-17   Sanders        NaN
## 383  2015-01-18   Sanders        NaN
## 384  2015-01-19   Sanders        NaN
## 385  2015-01-20   Sanders        NaN
## 386  2015-01-21   Sanders        NaN
## 387  2015-01-22   Sanders  2.0000000
## 388  2015-01-23   Sanders  2.0000000
## 389  2015-01-24   Sanders  2.0000000
## 390  2015-01-25   Sanders  2.5000000
## 391  2015-01-26   Sanders  3.0000000
## 392  2015-01-27   Sanders  3.0000000
## 393  2015-01-28   Sanders        NaN
## 394  2015-01-29   Sanders        NaN
## 395  2015-01-30   Sanders        NaN
## 396  2015-01-31   Sanders        NaN
## 397  2015-02-01   Sanders        NaN
## 398  2015-02-02   Sanders        NaN
## 399  2015-02-03   Sanders        NaN
## 400  2015-02-04   Sanders        NaN
## 401  2015-02-05   Sanders        NaN
## 402  2015-02-06   Sanders        NaN
## 403  2015-02-07   Sanders        NaN
## 404  2015-02-08   Sanders        NaN
## 405  2015-02-09   Sanders        NaN
## 406  2015-02-10   Sanders        NaN
## 407  2015-02-11   Sanders        NaN
## 408  2015-02-12   Sanders  3.0000000
## 409  2015-02-13   Sanders  3.0000000
## 410  2015-02-14   Sanders  3.0000000
## 411  2015-02-15   Sanders  3.0000000
## 412  2015-02-16   Sanders        NaN
## 413  2015-02-17   Sanders        NaN
## 414  2015-02-18   Sanders        NaN
## 415  2015-02-19   Sanders        NaN
## 416  2015-02-20   Sanders  5.0000000
## 417  2015-02-21   Sanders  6.5000000
## 418  2015-02-22   Sanders  6.5000000
## 419  2015-02-23   Sanders  8.0000000
## 420  2015-02-24   Sanders        NaN
## 421  2015-02-25   Sanders        NaN
## 422  2015-02-26   Sanders  4.0000000
## 423  2015-02-27   Sanders  4.0000000
## 424  2015-02-28   Sanders  4.0000000
## 425  2015-03-01   Sanders  4.5000000
## 426  2015-03-02   Sanders  4.5000000
## 427  2015-03-03   Sanders  5.0000000
## 428  2015-03-04   Sanders  5.0000000
## 429  2015-03-05   Sanders        NaN
## 430  2015-03-06   Sanders        NaN
## 431  2015-03-07   Sanders  7.0000000
## 432  2015-03-08   Sanders  7.0000000
## 433  2015-03-09   Sanders  7.0000000
## 434  2015-03-10   Sanders        NaN
## 435  2015-03-11   Sanders        NaN
## 436  2015-03-12   Sanders        NaN
## 437  2015-03-13   Sanders  3.0000000
## 438  2015-03-14   Sanders  6.0000000
## 439  2015-03-15   Sanders  6.0000000
## 440  2015-03-16   Sanders  9.0000000
## 441  2015-03-17   Sanders        NaN
## 442  2015-03-18   Sanders        NaN
## 443  2015-03-19   Sanders        NaN
## 444  2015-03-20   Sanders        NaN
## 445  2015-03-21   Sanders  7.0000000
## 446  2015-03-22   Sanders  7.0000000
## 447  2015-03-23   Sanders  7.0000000
## 448  2015-03-24   Sanders        NaN
## 449  2015-03-25   Sanders        NaN
## 450  2015-03-26   Sanders  5.5000000
## 451  2015-03-27   Sanders  5.5000000
## 452  2015-03-28   Sanders  5.5000000
## 453  2015-03-29   Sanders  4.6666667
## 454  2015-03-30   Sanders  5.3333333
## 455  2015-03-31   Sanders  5.3333333
## 456  2015-04-01   Sanders  7.0000000
## 457  2015-04-02   Sanders  7.0000000
## 458  2015-04-03   Sanders        NaN
## 459  2015-04-04   Sanders        NaN
## 460  2015-04-05   Sanders        NaN
## 461  2015-04-06   Sanders        NaN
## 462  2015-04-07   Sanders        NaN
## 463  2015-04-08   Sanders        NaN
## 464  2015-04-09   Sanders        NaN
## 465  2015-04-10   Sanders        NaN
## 466  2015-04-11   Sanders  7.0000000
## 467  2015-04-12   Sanders  7.0000000
## 468  2015-04-13   Sanders  7.0000000
## 469  2015-04-14   Sanders        NaN
## 470  2015-04-15   Sanders        NaN
## 471  2015-04-16   Sanders  6.5000000
## 472  2015-04-17   Sanders  6.5000000
## 473  2015-04-18   Sanders  7.0000000
## 474  2015-04-19   Sanders  6.2500000
## 475  2015-04-20   Sanders  6.6666667
## 476  2015-04-21   Sanders  6.0000000
## 477  2015-04-22   Sanders        NaN
## 478  2015-04-23   Sanders        NaN
## 479  2015-04-24   Sanders        NaN
## 480  2015-04-25   Sanders 10.0000000
## 481  2015-04-26   Sanders 10.0000000
## 482  2015-04-27   Sanders 10.0000000
## 483  2015-04-28   Sanders        NaN
## 484  2015-04-29   Sanders        NaN
## 485  2015-04-30   Sanders        NaN
## 486  2015-05-01   Sanders        NaN
## 487  2015-05-02   Sanders 17.0000000
## 488  2015-05-03   Sanders 17.0000000
## 489  2015-05-04   Sanders 17.0000000
## 490  2015-05-05   Sanders        NaN
## 491  2015-05-06   Sanders        NaN
## 492  2015-05-07   Sanders 13.0000000
## 493  2015-05-08   Sanders  9.0000000
## 494  2015-05-09   Sanders 10.0000000
## 495  2015-05-10   Sanders 10.0000000
## 496  2015-05-11   Sanders  9.0000000
## 497  2015-05-12   Sanders  5.5000000
## 498  2015-05-13   Sanders  5.0000000
## 499  2015-05-14   Sanders  5.0000000
## 500  2015-05-15   Sanders  5.0000000
## 501  2015-05-16   Sanders  8.5000000
## 502  2015-05-17   Sanders 12.0000000
## 503  2015-05-18   Sanders 12.0000000
## 504  2015-05-19   Sanders 15.0000000
## 505  2015-05-20   Sanders 15.0000000
## 506  2015-05-21   Sanders 15.0000000
## 507  2015-05-22   Sanders 15.0000000
## 508  2015-05-23   Sanders 16.0000000
## 509  2015-05-24   Sanders 16.0000000
## 510  2015-05-25   Sanders 16.0000000
## 511  2015-05-26   Sanders 15.0000000
## 512  2015-05-27   Sanders        NaN
## 513  2015-05-28   Sanders 10.0000000
## 514  2015-05-29   Sanders 10.0000000
## 515  2015-05-30   Sanders 10.3333333
## 516  2015-05-31   Sanders 10.5000000
## 517  2015-06-01   Sanders 11.0000000
## 518  2015-06-02   Sanders 11.0000000
## 519  2015-06-03   Sanders        NaN
## 520  2015-06-04   Sanders        NaN
## 521  2015-06-05   Sanders        NaN
## 522  2015-06-06   Sanders 11.0000000
## 523  2015-06-07   Sanders 11.0000000
## 524  2015-06-08   Sanders 11.0000000
## 525  2015-06-09   Sanders        NaN
## 526  2015-06-10   Sanders        NaN
## 527  2015-06-11   Sanders 10.5000000
## 528  2015-06-12   Sanders 10.5000000
## 529  2015-06-13   Sanders 12.3333333
## 530  2015-06-14   Sanders 13.0000000
## 531  2015-06-15   Sanders 15.5000000
## 532  2015-06-16   Sanders 15.0000000
## 533  2015-06-17   Sanders 15.0000000
## 534  2015-06-18   Sanders 15.0000000
## 535  2015-06-19   Sanders        NaN
## 536  2015-06-20   Sanders 18.0000000
## 537  2015-06-21   Sanders 16.5000000
## 538  2015-06-22   Sanders 16.5000000
## 539  2015-06-23   Sanders 15.0000000
## 540  2015-06-24   Sanders        NaN
## 541  2015-06-25   Sanders        NaN
## 542  2015-06-26   Sanders 15.0000000
## 543  2015-06-27   Sanders 17.0000000
## 544  2015-06-28   Sanders 17.0000000
## 545  2015-06-29   Sanders 19.0000000
## 546  2015-06-30   Sanders        NaN
## 547  2015-07-01   Sanders        NaN
## 548  2015-07-02   Sanders        NaN
## 549  2015-07-03   Sanders        NaN
## 550  2015-07-04   Sanders 24.0000000
## 551  2015-07-05   Sanders 24.0000000
## 552  2015-07-06   Sanders 24.0000000
## 553  2015-07-07   Sanders        NaN
## 554  2015-07-08   Sanders        NaN
## 555  2015-07-09   Sanders 15.6666667
## 556  2015-07-10   Sanders 15.6666667
## 557  2015-07-11   Sanders 15.6666667
## 558  2015-07-12   Sanders 15.6666667
## 559  2015-07-13   Sanders 17.5000000
## 560  2015-07-14   Sanders 17.5000000
## 561  2015-07-15   Sanders 19.0000000
## 562  2015-07-16   Sanders 14.0000000
## 563  2015-07-17   Sanders 15.0000000
## 564  2015-07-18   Sanders 16.0000000
## 565  2015-07-19   Sanders 16.0000000
## 566  2015-07-20   Sanders 18.6666667
## 567  2015-07-21   Sanders 22.0000000
## 568  2015-07-22   Sanders 19.0000000
## 569  2015-07-23   Sanders 18.0000000
## 570  2015-07-24   Sanders 18.0000000
## 571  2015-07-25   Sanders 17.0000000
## 572  2015-07-26   Sanders 22.5000000
## 573  2015-07-27   Sanders 22.5000000
## 574  2015-07-28   Sanders 22.5000000
## 575  2015-07-29   Sanders 19.3333333
## 576  2015-07-30   Sanders 21.0000000
## 577  2015-07-31   Sanders 18.7500000
## 578  2015-08-01   Sanders 18.4000000
## 579  2015-08-02   Sanders 18.4000000
## 580  2015-08-03   Sanders 18.0000000
## 581  2015-08-04   Sanders 19.0000000
## 582  2015-08-05   Sanders 17.0000000
## 583  2015-08-06   Sanders        NaN
## 584  2015-08-07   Sanders 19.0000000
## 585  2015-08-08   Sanders 18.5000000
## 586  2015-08-09   Sanders 18.5000000
## 587  2015-08-10   Sanders 18.0000000
## 588  2015-08-11   Sanders 24.0000000
## 589  2015-08-12   Sanders 24.0000000
## 590  2015-08-13   Sanders 29.5000000
## 591  2015-08-14   Sanders 26.3333333
## 592  2015-08-15   Sanders 25.5000000
## 593  2015-08-16   Sanders 25.5000000
## 594  2015-08-17   Sanders 24.5000000
## 595  2015-08-18   Sanders 24.5000000
## 596  2015-08-19   Sanders 23.0000000
## 597  2015-08-20   Sanders 22.0000000
## 598  2015-08-21   Sanders 21.5000000
## 599  2015-08-22   Sanders 21.6666667
## 600  2015-08-23   Sanders 22.0000000
## 601  2015-08-24   Sanders 22.0000000
## 602  2015-08-25   Sanders 22.0000000
## 603  2015-08-26   Sanders 22.0000000
## 604  2015-08-27   Sanders        NaN
## 605  2015-08-28   Sanders 22.6666667
## 606  2015-08-29   Sanders 23.0000000
## 607  2015-08-30   Sanders 23.0000000
## 608  2015-08-31   Sanders 23.0000000
## 609  2015-09-01   Sanders 23.0000000
## 610  2015-09-02   Sanders 22.0000000
## 611  2015-09-03   Sanders        NaN
## 612  2015-09-04   Sanders 27.0000000
## 613  2015-09-05   Sanders 25.7500000
## 614  2015-09-06   Sanders 25.7500000
## 615  2015-09-07   Sanders 25.4000000
## 616  2015-09-08   Sanders 25.0000000
## 617  2015-09-09   Sanders 26.0000000
## 618  2015-09-10   Sanders 24.0000000
## 619  2015-09-11   Sanders 22.3333333
## 620  2015-09-12   Sanders 23.0000000
## 621  2015-09-13   Sanders 23.0000000
## 622  2015-09-14   Sanders 22.6666667
## 623  2015-09-15   Sanders 22.6666667
## 624  2015-09-16   Sanders 23.6666667
## 625  2015-09-17   Sanders 23.7500000
## 626  2015-09-18   Sanders 24.4285714
## 627  2015-09-19   Sanders 24.5714286
## 628  2015-09-20   Sanders 27.0000000
## 629  2015-09-21   Sanders 26.8333333
## 630  2015-09-22   Sanders 28.0000000
## 631  2015-09-23   Sanders 27.3333333
## 632  2015-09-24   Sanders 24.5000000
## 633  2015-09-25   Sanders 22.0000000
## 634  2015-09-26   Sanders 21.8333333
## 635  2015-09-27   Sanders 21.8333333
## 636  2015-09-28   Sanders 21.6000000
## 637  2015-09-29   Sanders 21.2500000
## 638  2015-09-30   Sanders 21.2500000
## 639  2015-10-01   Sanders 21.4000000
## 640  2015-10-02   Sanders 22.7500000
## 641  2015-10-03   Sanders 23.6000000
## 642  2015-10-04   Sanders 23.6000000
## 643  2015-10-05   Sanders 25.6666667
## 644  2015-10-06   Sanders 27.0000000
## 645  2015-10-07   Sanders 27.0000000
## 646  2015-10-08   Sanders 22.5000000
## 647  2015-10-09   Sanders 22.5000000
## 648  2015-10-10   Sanders 23.5000000
## 649  2015-10-11   Sanders 23.5000000
## 650  2015-10-12   Sanders 23.5000000
## 651  2015-10-13   Sanders 27.5000000
## 652  2015-10-14   Sanders 28.0000000
## 653  2015-10-15   Sanders 26.1666667
## 654  2015-10-16   Sanders 24.3333333
## 655  2015-10-17   Sanders 25.0000000
## 656  2015-10-18   Sanders 25.2000000
## 657  2015-10-19   Sanders 26.5000000
## 658  2015-10-20   Sanders 29.0000000
## 659  2015-10-21   Sanders 29.0000000
## 660  2015-10-22   Sanders 26.0000000
## 661  2015-10-23   Sanders 27.5000000
## 662  2015-10-24   Sanders 30.2500000
## 663  2015-10-25   Sanders 30.4000000
## 664  2015-10-26   Sanders 28.4000000
## 665  2015-10-27   Sanders 31.2000000
## 666  2015-10-28   Sanders 31.7500000
## 667  2015-10-29   Sanders 31.1428571
## 668  2015-10-30   Sanders 31.0000000
## 669  2015-10-31   Sanders 30.4000000
## 670  2015-11-01   Sanders 30.5000000
## 671  2015-11-02   Sanders 31.4000000
## 672  2015-11-03   Sanders 30.5000000
## 673  2015-11-04   Sanders 31.5000000
## 674  2015-11-05   Sanders 29.5000000
## 675  2015-11-06   Sanders 29.3333333
## 676  2015-11-07   Sanders 30.7500000
## 677  2015-11-08   Sanders 30.7500000
## 678  2015-11-09   Sanders 31.6666667
## 679  2015-11-10   Sanders 32.0000000
## 680  2015-11-11   Sanders 35.0000000
## 681  2015-11-12   Sanders        NaN
## 682  2015-11-13   Sanders 26.0000000
## 683  2015-11-14   Sanders 28.5000000
## 684  2015-11-15   Sanders 30.0000000
## 685  2015-11-16   Sanders 30.2857143
## 686  2015-11-17   Sanders 31.0000000
## 687  2015-11-18   Sanders 32.3333333
## 688  2015-11-19   Sanders 33.3333333
## 689  2015-11-20   Sanders 34.0000000
## 690  2015-11-21   Sanders 32.0000000
## 691  2015-11-22   Sanders 32.0000000
## 692  2015-11-23   Sanders 31.5000000
## 693  2015-11-24   Sanders 30.0000000
## 694  2015-11-25   Sanders 30.0000000
## 695  2015-11-26   Sanders 30.0000000
## 696  2015-11-27   Sanders 30.0000000
## 697  2015-11-28   Sanders 32.0000000
## 698  2015-11-29   Sanders 32.0000000
## 699  2015-11-30   Sanders 32.2500000
## 700  2015-12-01   Sanders 33.0000000
## 701  2015-12-02   Sanders 32.2500000
## 702  2015-12-03   Sanders 29.0000000
## 703  2015-12-04   Sanders 30.2000000
## 704  2015-12-05   Sanders 29.6000000
## 705  2015-12-06   Sanders 30.8333333
## 706  2015-12-07   Sanders 31.0000000
## 707  2015-12-08   Sanders 33.0000000
## 708  2015-12-09   Sanders 34.0000000
## 709  2015-12-10   Sanders 27.0000000
## 710  2015-12-11   Sanders 27.0000000
## 711  2015-12-12   Sanders 27.5000000
## 712  2015-12-13   Sanders 27.5000000
## 713  2015-12-14   Sanders 28.0000000
## 714  2015-12-15   Sanders 28.0000000
## 715  2015-12-16   Sanders 30.3333333
## 716  2015-12-17   Sanders 31.0000000
## 717  2015-12-18   Sanders        NaN
## 718  2015-12-19   Sanders        NaN
## 719  2015-12-20   Sanders        NaN
## 720  2015-12-21   Sanders        NaN
## 721  2015-12-22   Sanders        NaN
## 722  2015-12-23   Sanders        NaN
## 723  2015-12-24   Sanders        NaN
## 724  2015-12-25   Sanders        NaN
## 725  2015-12-26   Sanders        NaN
## 726  2015-12-27   Sanders        NaN
## 727  2015-12-28   Sanders        NaN
## 728  2015-12-29   Sanders        NaN
## 729  2015-12-30   Sanders        NaN
## 730  2015-12-31   Sanders        NaN
## 731  2015-01-01  O.Malley        NaN
## 732  2015-01-02  O.Malley        NaN
## 733  2015-01-03  O.Malley        NaN
## 734  2015-01-04  O.Malley        NaN
## 735  2015-01-05  O.Malley        NaN
## 736  2015-01-06  O.Malley        NaN
## 737  2015-01-07  O.Malley        NaN
## 738  2015-01-08  O.Malley        NaN
## 739  2015-01-09  O.Malley        NaN
## 740  2015-01-10  O.Malley  0.0000000
## 741  2015-01-11  O.Malley  0.0000000
## 742  2015-01-12  O.Malley  0.0000000
## 743  2015-01-13  O.Malley        NaN
## 744  2015-01-14  O.Malley        NaN
## 745  2015-01-15  O.Malley        NaN
## 746  2015-01-16  O.Malley        NaN
## 747  2015-01-17  O.Malley        NaN
## 748  2015-01-18  O.Malley        NaN
## 749  2015-01-19  O.Malley        NaN
## 750  2015-01-20  O.Malley        NaN
## 751  2015-01-21  O.Malley        NaN
## 752  2015-01-22  O.Malley  1.0000000
## 753  2015-01-23  O.Malley  1.0000000
## 754  2015-01-24  O.Malley  1.0000000
## 755  2015-01-25  O.Malley  1.5000000
## 756  2015-01-26  O.Malley  2.0000000
## 757  2015-01-27  O.Malley  2.0000000
## 758  2015-01-28  O.Malley        NaN
## 759  2015-01-29  O.Malley        NaN
## 760  2015-01-30  O.Malley        NaN
## 761  2015-01-31  O.Malley        NaN
## 762  2015-02-01  O.Malley        NaN
## 763  2015-02-02  O.Malley        NaN
## 764  2015-02-03  O.Malley        NaN
## 765  2015-02-04  O.Malley        NaN
## 766  2015-02-05  O.Malley        NaN
## 767  2015-02-06  O.Malley        NaN
## 768  2015-02-07  O.Malley        NaN
## 769  2015-02-08  O.Malley        NaN
## 770  2015-02-09  O.Malley        NaN
## 771  2015-02-10  O.Malley        NaN
## 772  2015-02-11  O.Malley        NaN
## 773  2015-02-12  O.Malley  1.0000000
## 774  2015-02-13  O.Malley  1.0000000
## 775  2015-02-14  O.Malley  1.0000000
## 776  2015-02-15  O.Malley  1.0000000
## 777  2015-02-16  O.Malley        NaN
## 778  2015-02-17  O.Malley        NaN
## 779  2015-02-18  O.Malley        NaN
## 780  2015-02-19  O.Malley        NaN
## 781  2015-02-20  O.Malley  1.0000000
## 782  2015-02-21  O.Malley  0.5000000
## 783  2015-02-22  O.Malley  0.5000000
## 784  2015-02-23  O.Malley  0.0000000
## 785  2015-02-24  O.Malley        NaN
## 786  2015-02-25  O.Malley        NaN
## 787  2015-02-26  O.Malley  0.0000000
## 788  2015-02-27  O.Malley  0.0000000
## 789  2015-02-28  O.Malley  0.0000000
## 790  2015-03-01  O.Malley  0.5000000
## 791  2015-03-02  O.Malley  0.5000000
## 792  2015-03-03  O.Malley  1.0000000
## 793  2015-03-04  O.Malley  1.0000000
## 794  2015-03-05  O.Malley        NaN
## 795  2015-03-06  O.Malley        NaN
## 796  2015-03-07  O.Malley  4.0000000
## 797  2015-03-08  O.Malley  4.0000000
## 798  2015-03-09  O.Malley  4.0000000
## 799  2015-03-10  O.Malley        NaN
## 800  2015-03-11  O.Malley        NaN
## 801  2015-03-12  O.Malley        NaN
## 802  2015-03-13  O.Malley  1.0000000
## 803  2015-03-14  O.Malley  1.5000000
## 804  2015-03-15  O.Malley  1.5000000
## 805  2015-03-16  O.Malley  2.0000000
## 806  2015-03-17  O.Malley        NaN
## 807  2015-03-18  O.Malley        NaN
## 808  2015-03-19  O.Malley        NaN
## 809  2015-03-20  O.Malley        NaN
## 810  2015-03-21  O.Malley  3.0000000
## 811  2015-03-22  O.Malley  3.0000000
## 812  2015-03-23  O.Malley  3.0000000
## 813  2015-03-24  O.Malley        NaN
## 814  2015-03-25  O.Malley        NaN
## 815  2015-03-26  O.Malley  1.5000000
## 816  2015-03-27  O.Malley  1.5000000
## 817  2015-03-28  O.Malley  1.5000000
## 818  2015-03-29  O.Malley  1.6666667
## 819  2015-03-30  O.Malley  2.3333333
## 820  2015-03-31  O.Malley  2.3333333
## 821  2015-04-01  O.Malley  2.0000000
## 822  2015-04-02  O.Malley  2.0000000
## 823  2015-04-03  O.Malley        NaN
## 824  2015-04-04  O.Malley        NaN
## 825  2015-04-05  O.Malley        NaN
## 826  2015-04-06  O.Malley        NaN
## 827  2015-04-07  O.Malley        NaN
## 828  2015-04-08  O.Malley        NaN
## 829  2015-04-09  O.Malley        NaN
## 830  2015-04-10  O.Malley        NaN
## 831  2015-04-11  O.Malley  1.0000000
## 832  2015-04-12  O.Malley  1.0000000
## 833  2015-04-13  O.Malley  1.0000000
## 834  2015-04-14  O.Malley        NaN
## 835  2015-04-15  O.Malley        NaN
## 836  2015-04-16  O.Malley  2.0000000
## 837  2015-04-17  O.Malley  2.0000000
## 838  2015-04-18  O.Malley  2.0000000
## 839  2015-04-19  O.Malley  1.7500000
## 840  2015-04-20  O.Malley  2.0000000
## 841  2015-04-21  O.Malley  2.0000000
## 842  2015-04-22  O.Malley        NaN
## 843  2015-04-23  O.Malley        NaN
## 844  2015-04-24  O.Malley        NaN
## 845  2015-04-25  O.Malley  2.0000000
## 846  2015-04-26  O.Malley  2.0000000
## 847  2015-04-27  O.Malley  2.0000000
## 848  2015-04-28  O.Malley        NaN
## 849  2015-04-29  O.Malley        NaN
## 850  2015-04-30  O.Malley        NaN
## 851  2015-05-01  O.Malley        NaN
## 852  2015-05-02  O.Malley  2.0000000
## 853  2015-05-03  O.Malley  2.0000000
## 854  2015-05-04  O.Malley  2.0000000
## 855  2015-05-05  O.Malley        NaN
## 856  2015-05-06  O.Malley        NaN
## 857  2015-05-07  O.Malley  2.0000000
## 858  2015-05-08  O.Malley  1.5000000
## 859  2015-05-09  O.Malley  0.7500000
## 860  2015-05-10  O.Malley  0.7500000
## 861  2015-05-11  O.Malley  0.3333333
## 862  2015-05-12  O.Malley  0.5000000
## 863  2015-05-13  O.Malley  1.0000000
## 864  2015-05-14  O.Malley  1.0000000
## 865  2015-05-15  O.Malley  1.0000000
## 866  2015-05-16  O.Malley  1.5000000
## 867  2015-05-17  O.Malley  2.0000000
## 868  2015-05-18  O.Malley  2.0000000
## 869  2015-05-19  O.Malley  1.0000000
## 870  2015-05-20  O.Malley  1.0000000
## 871  2015-05-21  O.Malley  1.0000000
## 872  2015-05-22  O.Malley  1.0000000
## 873  2015-05-23  O.Malley  2.0000000
## 874  2015-05-24  O.Malley  2.0000000
## 875  2015-05-25  O.Malley  2.0000000
## 876  2015-05-26  O.Malley  1.0000000
## 877  2015-05-27  O.Malley        NaN
## 878  2015-05-28  O.Malley  3.0000000
## 879  2015-05-29  O.Malley  2.0000000
## 880  2015-05-30  O.Malley  2.0000000
## 881  2015-05-31  O.Malley  2.5000000
## 882  2015-06-01  O.Malley  3.0000000
## 883  2015-06-02  O.Malley  4.0000000
## 884  2015-06-03  O.Malley        NaN
## 885  2015-06-04  O.Malley        NaN
## 886  2015-06-05  O.Malley        NaN
## 887  2015-06-06  O.Malley  1.0000000
## 888  2015-06-07  O.Malley  1.0000000
## 889  2015-06-08  O.Malley  1.0000000
## 890  2015-06-09  O.Malley        NaN
## 891  2015-06-10  O.Malley        NaN
## 892  2015-06-11  O.Malley  3.0000000
## 893  2015-06-12  O.Malley  3.0000000
## 894  2015-06-13  O.Malley  2.3333333
## 895  2015-06-14  O.Malley  2.2500000
## 896  2015-06-15  O.Malley  1.5000000
## 897  2015-06-16  O.Malley  2.0000000
## 898  2015-06-17  O.Malley  2.0000000
## 899  2015-06-18  O.Malley  2.0000000
## 900  2015-06-19  O.Malley        NaN
## 901  2015-06-20  O.Malley  2.0000000
## 902  2015-06-21  O.Malley  1.5000000
## 903  2015-06-22  O.Malley  1.5000000
## 904  2015-06-23  O.Malley  1.0000000
## 905  2015-06-24  O.Malley        NaN
## 906  2015-06-25  O.Malley        NaN
## 907  2015-06-26  O.Malley  1.0000000
## 908  2015-06-27  O.Malley  1.5000000
## 909  2015-06-28  O.Malley  1.5000000
## 910  2015-06-29  O.Malley  2.0000000
## 911  2015-06-30  O.Malley        NaN
## 912  2015-07-01  O.Malley        NaN
## 913  2015-07-02  O.Malley        NaN
## 914  2015-07-03  O.Malley        NaN
## 915  2015-07-04  O.Malley  0.0000000
## 916  2015-07-05  O.Malley  0.0000000
## 917  2015-07-06  O.Malley  0.0000000
## 918  2015-07-07  O.Malley        NaN
## 919  2015-07-08  O.Malley        NaN
## 920  2015-07-09  O.Malley  0.6666667
## 921  2015-07-10  O.Malley  0.6666667
## 922  2015-07-11  O.Malley  0.6666667
## 923  2015-07-12  O.Malley  0.6666667
## 924  2015-07-13  O.Malley  1.0000000
## 925  2015-07-14  O.Malley  1.0000000
## 926  2015-07-15  O.Malley  1.0000000
## 927  2015-07-16  O.Malley  1.0000000
## 928  2015-07-17  O.Malley  0.5000000
## 929  2015-07-18  O.Malley  0.3333333
## 930  2015-07-19  O.Malley  0.3333333
## 931  2015-07-20  O.Malley  0.6666667
## 932  2015-07-21  O.Malley  2.0000000
## 933  2015-07-22  O.Malley  0.0000000
## 934  2015-07-23  O.Malley  0.5000000
## 935  2015-07-24  O.Malley  0.5000000
## 936  2015-07-25  O.Malley  0.6666667
## 937  2015-07-26  O.Malley  1.7500000
## 938  2015-07-27  O.Malley  1.7500000
## 939  2015-07-28  O.Malley  1.7500000
## 940  2015-07-29  O.Malley  2.3333333
## 941  2015-07-30  O.Malley  2.0000000
## 942  2015-07-31  O.Malley  1.5000000
## 943  2015-08-01  O.Malley  1.4000000
## 944  2015-08-02  O.Malley  1.4000000
## 945  2015-08-03  O.Malley  1.3333333
## 946  2015-08-04  O.Malley  1.0000000
## 947  2015-08-05  O.Malley  1.0000000
## 948  2015-08-06  O.Malley        NaN
## 949  2015-08-07  O.Malley  4.0000000
## 950  2015-08-08  O.Malley  3.0000000
## 951  2015-08-09  O.Malley  3.0000000
## 952  2015-08-10  O.Malley  2.0000000
## 953  2015-08-11  O.Malley  1.5000000
## 954  2015-08-12  O.Malley  1.5000000
## 955  2015-08-13  O.Malley  1.5000000
## 956  2015-08-14  O.Malley  2.0000000
## 957  2015-08-15  O.Malley  1.7500000
## 958  2015-08-16  O.Malley  1.7500000
## 959  2015-08-17  O.Malley  1.0000000
## 960  2015-08-18  O.Malley  1.0000000
## 961  2015-08-19  O.Malley  1.0000000
## 962  2015-08-20  O.Malley  1.0000000
## 963  2015-08-21  O.Malley  1.5000000
## 964  2015-08-22  O.Malley  1.3333333
## 965  2015-08-23  O.Malley  1.0000000
## 966  2015-08-24  O.Malley  1.0000000
## 967  2015-08-25  O.Malley  1.0000000
## 968  2015-08-26  O.Malley  1.0000000
## 969  2015-08-27  O.Malley        NaN
## 970  2015-08-28  O.Malley  2.3333333
## 971  2015-08-29  O.Malley  1.7500000
## 972  2015-08-30  O.Malley  1.7500000
## 973  2015-08-31  O.Malley  1.0000000
## 974  2015-09-01  O.Malley  1.0000000
## 975  2015-09-02  O.Malley  0.5000000
## 976  2015-09-03  O.Malley        NaN
## 977  2015-09-04  O.Malley  2.0000000
## 978  2015-09-05  O.Malley  2.0000000
## 979  2015-09-06  O.Malley  2.0000000
## 980  2015-09-07  O.Malley  2.0000000
## 981  2015-09-08  O.Malley  2.2500000
## 982  2015-09-09  O.Malley  2.5000000
## 983  2015-09-10  O.Malley  2.0000000
## 984  2015-09-11  O.Malley  1.0000000
## 985  2015-09-12  O.Malley  0.7500000
## 986  2015-09-13  O.Malley  0.7500000
## 987  2015-09-14  O.Malley  0.3333333
## 988  2015-09-15  O.Malley  0.3333333
## 989  2015-09-16  O.Malley  0.3333333
## 990  2015-09-17  O.Malley  0.5000000
## 991  2015-09-18  O.Malley  1.1428571
## 992  2015-09-19  O.Malley  1.2857143
## 993  2015-09-20  O.Malley  1.0000000
## 994  2015-09-21  O.Malley  0.8333333
## 995  2015-09-22  O.Malley  1.0000000
## 996  2015-09-23  O.Malley  0.6666667
## 997  2015-09-24  O.Malley  0.0000000
## 998  2015-09-25  O.Malley  0.5000000
## 999  2015-09-26  O.Malley  0.6000000
## 1000 2015-09-27  O.Malley  0.6000000
## 1001 2015-09-28  O.Malley  0.7500000
## 1002 2015-09-29  O.Malley  1.0000000
## 1003 2015-09-30  O.Malley  1.0000000
## 1004 2015-10-01  O.Malley  1.0000000
## 1005 2015-10-02  O.Malley  0.7500000
## 1006 2015-10-03  O.Malley  1.0000000
## 1007 2015-10-04  O.Malley  1.0000000
## 1008 2015-10-05  O.Malley  1.3333333
## 1009 2015-10-06  O.Malley  2.0000000
## 1010 2015-10-07  O.Malley  2.0000000
## 1011 2015-10-08  O.Malley  1.5000000
## 1012 2015-10-09  O.Malley  1.5000000
## 1013 2015-10-10  O.Malley  1.5000000
## 1014 2015-10-11  O.Malley  1.5000000
## 1015 2015-10-12  O.Malley  1.5000000
## 1016 2015-10-13  O.Malley  1.5000000
## 1017 2015-10-14  O.Malley  1.0000000
## 1018 2015-10-15  O.Malley  0.8333333
## 1019 2015-10-16  O.Malley  1.1666667
## 1020 2015-10-17  O.Malley  1.1428571
## 1021 2015-10-18  O.Malley  1.0000000
## 1022 2015-10-19  O.Malley  1.0000000
## 1023 2015-10-20  O.Malley  1.0000000
## 1024 2015-10-21  O.Malley  1.0000000
## 1025 2015-10-22  O.Malley  5.0000000
## 1026 2015-10-23  O.Malley  3.5000000
## 1027 2015-10-24  O.Malley  2.7500000
## 1028 2015-10-25  O.Malley  2.8000000
## 1029 2015-10-26  O.Malley  2.4000000
## 1030 2015-10-27  O.Malley  2.0000000
## 1031 2015-10-28  O.Malley  2.0000000
## 1032 2015-10-29  O.Malley  2.0000000
## 1033 2015-10-30  O.Malley  2.0000000
## 1034 2015-10-31  O.Malley  2.8000000
## 1035 2015-11-01  O.Malley  2.6666667
## 1036 2015-11-02  O.Malley  2.8000000
## 1037 2015-11-03  O.Malley  3.5000000
## 1038 2015-11-04  O.Malley  5.0000000
## 1039 2015-11-05  O.Malley  2.5000000
## 1040 2015-11-06  O.Malley  2.3333333
## 1041 2015-11-07  O.Malley  2.5000000
## 1042 2015-11-08  O.Malley  2.5000000
## 1043 2015-11-09  O.Malley  2.3333333
## 1044 2015-11-10  O.Malley  2.5000000
## 1045 2015-11-11  O.Malley  3.0000000
## 1046 2015-11-12  O.Malley        NaN
## 1047 2015-11-13  O.Malley  2.0000000
## 1048 2015-11-14  O.Malley  3.0000000
## 1049 2015-11-15  O.Malley  2.5000000
## 1050 2015-11-16  O.Malley  3.2857143
## 1051 2015-11-17  O.Malley  3.5000000
## 1052 2015-11-18  O.Malley  3.3333333
## 1053 2015-11-19  O.Malley  3.3333333
## 1054 2015-11-20  O.Malley  4.0000000
## 1055 2015-11-21  O.Malley  5.0000000
## 1056 2015-11-22  O.Malley  5.0000000
## 1057 2015-11-23  O.Malley  5.0000000
## 1058 2015-11-24  O.Malley  4.0000000
## 1059 2015-11-25  O.Malley  4.0000000
## 1060 2015-11-26  O.Malley  2.0000000
## 1061 2015-11-27  O.Malley  2.0000000
## 1062 2015-11-28  O.Malley  2.6666667
## 1063 2015-11-29  O.Malley  2.6666667
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## 1065 2015-12-01  O.Malley  2.3333333
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## 1067 2015-12-03  O.Malley  2.7500000
## 1068 2015-12-04  O.Malley  2.6000000
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## 1070 2015-12-06  O.Malley  3.5000000
## 1071 2015-12-07  O.Malley  4.4000000
## 1072 2015-12-08  O.Malley  5.0000000
## 1073 2015-12-09  O.Malley  3.6666667
## 1074 2015-12-10  O.Malley  4.5000000
## 1075 2015-12-11  O.Malley  3.6666667
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## 1077 2015-12-13  O.Malley  3.5000000
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## 1083 2015-12-19  O.Malley        NaN
## 1084 2015-12-20  O.Malley        NaN
## 1085 2015-12-21  O.Malley        NaN
## 1086 2015-12-22  O.Malley        NaN
## 1087 2015-12-23  O.Malley        NaN
## 1088 2015-12-24  O.Malley        NaN
## 1089 2015-12-25  O.Malley        NaN
## 1090 2015-12-26  O.Malley        NaN
## 1091 2015-12-27  O.Malley        NaN
## 1092 2015-12-28  O.Malley        NaN
## 1093 2015-12-29  O.Malley        NaN
## 1094 2015-12-30  O.Malley        NaN
## 1095 2015-12-31  O.Malley        NaN
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## 1101 2015-01-06     Biden        NaN
## 1102 2015-01-07     Biden        NaN
## 1103 2015-01-08     Biden        NaN
## 1104 2015-01-09     Biden        NaN
## 1105 2015-01-10     Biden  7.0000000
## 1106 2015-01-11     Biden  7.0000000
## 1107 2015-01-12     Biden  7.0000000
## 1108 2015-01-13     Biden        NaN
## 1109 2015-01-14     Biden        NaN
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## 1113 2015-01-18     Biden        NaN
## 1114 2015-01-19     Biden        NaN
## 1115 2015-01-20     Biden        NaN
## 1116 2015-01-21     Biden        NaN
## 1117 2015-01-22     Biden 15.0000000
## 1118 2015-01-23     Biden 15.0000000
## 1119 2015-01-24     Biden 15.0000000
## 1120 2015-01-25     Biden 16.0000000
## 1121 2015-01-26     Biden 17.0000000
## 1122 2015-01-27     Biden 17.0000000
## 1123 2015-01-28     Biden        NaN
## 1124 2015-01-29     Biden        NaN
## 1125 2015-01-30     Biden        NaN
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## 1132 2015-02-06     Biden        NaN
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## 1134 2015-02-08     Biden        NaN
## 1135 2015-02-09     Biden        NaN
## 1136 2015-02-10     Biden        NaN
## 1137 2015-02-11     Biden        NaN
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## 1139 2015-02-13     Biden 14.0000000
## 1140 2015-02-14     Biden 14.0000000
## 1141 2015-02-15     Biden 14.0000000
## 1142 2015-02-16     Biden        NaN
## 1143 2015-02-17     Biden        NaN
## 1144 2015-02-18     Biden        NaN
## 1145 2015-02-19     Biden        NaN
## 1146 2015-02-20     Biden 16.0000000
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## 1148 2015-02-22     Biden 14.0000000
## 1149 2015-02-23     Biden 12.0000000
## 1150 2015-02-24     Biden        NaN
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## 1153 2015-02-27     Biden 10.0000000
## 1154 2015-02-28     Biden 10.0000000
## 1155 2015-03-01     Biden 11.5000000
## 1156 2015-03-02     Biden 11.5000000
## 1157 2015-03-03     Biden 13.0000000
## 1158 2015-03-04     Biden 13.0000000
## 1159 2015-03-05     Biden        NaN
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## 1161 2015-03-07     Biden 13.0000000
## 1162 2015-03-08     Biden 13.0000000
## 1163 2015-03-09     Biden 13.0000000
## 1164 2015-03-10     Biden        NaN
## 1165 2015-03-11     Biden        NaN
## 1166 2015-03-12     Biden        NaN
## 1167 2015-03-13     Biden 15.0000000
## 1168 2015-03-14     Biden 11.5000000
## 1169 2015-03-15     Biden 11.5000000
## 1170 2015-03-16     Biden  8.0000000
## 1171 2015-03-17     Biden        NaN
## 1172 2015-03-18     Biden        NaN
## 1173 2015-03-19     Biden        NaN
## 1174 2015-03-20     Biden        NaN
## 1175 2015-03-21     Biden 11.0000000
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## 1177 2015-03-23     Biden 11.0000000
## 1178 2015-03-24     Biden        NaN
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## 1182 2015-03-28     Biden  9.5000000
## 1183 2015-03-29     Biden 10.3333333
## 1184 2015-03-30     Biden 11.6666667
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## 1186 2015-04-01     Biden 16.0000000
## 1187 2015-04-02     Biden 16.0000000
## 1188 2015-04-03     Biden        NaN
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## 1190 2015-04-05     Biden        NaN
## 1191 2015-04-06     Biden        NaN
## 1192 2015-04-07     Biden        NaN
## 1193 2015-04-08     Biden        NaN
## 1194 2015-04-09     Biden        NaN
## 1195 2015-04-10     Biden        NaN
## 1196 2015-04-11     Biden  7.0000000
## 1197 2015-04-12     Biden  7.0000000
## 1198 2015-04-13     Biden  7.0000000
## 1199 2015-04-14     Biden        NaN
## 1200 2015-04-15     Biden        NaN
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## 1202 2015-04-17     Biden 10.5000000
## 1203 2015-04-18     Biden 10.0000000
## 1204 2015-04-19     Biden  9.7500000
## 1205 2015-04-20     Biden  9.3333333
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## 1207 2015-04-22     Biden        NaN
## 1208 2015-04-23     Biden        NaN
## 1209 2015-04-24     Biden        NaN
## 1210 2015-04-25     Biden  7.0000000
## 1211 2015-04-26     Biden  7.0000000
## 1212 2015-04-27     Biden  7.0000000
## 1213 2015-04-28     Biden        NaN
## 1214 2015-04-29     Biden        NaN
## 1215 2015-04-30     Biden        NaN
## 1216 2015-05-01     Biden        NaN
## 1217 2015-05-02     Biden 13.0000000
## 1218 2015-05-03     Biden 13.0000000
## 1219 2015-05-04     Biden 13.0000000
## 1220 2015-05-05     Biden        NaN
## 1221 2015-05-06     Biden        NaN
## 1222 2015-05-07     Biden        NaN
## 1223 2015-05-08     Biden  8.0000000
## 1224 2015-05-09     Biden  7.0000000
## 1225 2015-05-10     Biden  7.0000000
## 1226 2015-05-11     Biden  7.0000000
## 1227 2015-05-12     Biden  7.0000000
## 1228 2015-05-13     Biden  8.0000000
## 1229 2015-05-14     Biden  8.0000000
## 1230 2015-05-15     Biden  8.0000000
## 1231 2015-05-16     Biden  9.5000000
## 1232 2015-05-17     Biden 11.0000000
## 1233 2015-05-18     Biden 11.0000000
## 1234 2015-05-19     Biden  9.0000000
## 1235 2015-05-20     Biden  9.0000000
## 1236 2015-05-21     Biden  9.0000000
## 1237 2015-05-22     Biden  9.0000000
## 1238 2015-05-23     Biden  8.5000000
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## 1240 2015-05-25     Biden  8.5000000
## 1241 2015-05-26     Biden  9.0000000
## 1242 2015-05-27     Biden        NaN
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## 1244 2015-05-29     Biden 14.0000000
## 1245 2015-05-30     Biden 13.6666667
## 1246 2015-05-31     Biden 12.2500000
## 1247 2015-06-01     Biden 10.5000000
## 1248 2015-06-02     Biden  8.0000000
## 1249 2015-06-03     Biden        NaN
## 1250 2015-06-04     Biden        NaN
## 1251 2015-06-05     Biden        NaN
## 1252 2015-06-06     Biden 13.0000000
## 1253 2015-06-07     Biden 13.0000000
## 1254 2015-06-08     Biden 13.0000000
## 1255 2015-06-09     Biden        NaN
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## 1257 2015-06-11     Biden 12.0000000
## 1258 2015-06-12     Biden 12.0000000
## 1259 2015-06-13     Biden 12.5000000
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## 1261 2015-06-15     Biden 13.0000000
## 1262 2015-06-16     Biden        NaN
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## 1265 2015-06-19     Biden        NaN
## 1266 2015-06-20     Biden 11.0000000
## 1267 2015-06-21     Biden 11.0000000
## 1268 2015-06-22     Biden 11.0000000
## 1269 2015-06-23     Biden 11.0000000
## 1270 2015-06-24     Biden        NaN
## 1271 2015-06-25     Biden        NaN
## 1272 2015-06-26     Biden 17.0000000
## 1273 2015-06-27     Biden 13.0000000
## 1274 2015-06-28     Biden 13.0000000
## 1275 2015-06-29     Biden  9.0000000
## 1276 2015-06-30     Biden        NaN
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## 1278 2015-07-02     Biden        NaN
## 1279 2015-07-03     Biden        NaN
## 1280 2015-07-04     Biden  8.0000000
## 1281 2015-07-05     Biden  8.0000000
## 1282 2015-07-06     Biden  8.0000000
## 1283 2015-07-07     Biden        NaN
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## 1285 2015-07-09     Biden 10.5000000
## 1286 2015-07-10     Biden 10.5000000
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## 1288 2015-07-12     Biden 10.5000000
## 1289 2015-07-13     Biden  8.0000000
## 1290 2015-07-14     Biden  8.0000000
## 1291 2015-07-15     Biden  8.0000000
## 1292 2015-07-16     Biden 12.0000000
## 1293 2015-07-17     Biden 11.5000000
## 1294 2015-07-18     Biden 12.0000000
## 1295 2015-07-19     Biden 12.0000000
## 1296 2015-07-20     Biden 12.0000000
## 1297 2015-07-21     Biden        NaN
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## 1299 2015-07-23     Biden 14.0000000
## 1300 2015-07-24     Biden 14.0000000
## 1301 2015-07-25     Biden 13.3333333
## 1302 2015-07-26     Biden 11.3333333
## 1303 2015-07-27     Biden 11.3333333
## 1304 2015-07-28     Biden 11.3333333
## 1305 2015-07-29     Biden 10.0000000
## 1306 2015-07-30     Biden 12.5000000
## 1307 2015-07-31     Biden 12.6666667
## 1308 2015-08-01     Biden 13.0000000
## 1309 2015-08-02     Biden 13.0000000
## 1310 2015-08-03     Biden 13.5000000
## 1311 2015-08-04     Biden 13.5000000
## 1312 2015-08-05     Biden 14.0000000
## 1313 2015-08-06     Biden        NaN
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## 1315 2015-08-08     Biden 11.0000000
## 1316 2015-08-09     Biden 11.0000000
## 1317 2015-08-10     Biden 11.0000000
## 1318 2015-08-11     Biden 10.5000000
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## 1320 2015-08-13     Biden 12.0000000
## 1321 2015-08-14     Biden 14.0000000
## 1322 2015-08-15     Biden 13.3333333
## 1323 2015-08-16     Biden 13.3333333
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## 1328 2015-08-21     Biden 15.5000000
## 1329 2015-08-22     Biden 15.3333333
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## 1332 2015-08-25     Biden 16.5000000
## 1333 2015-08-26     Biden 15.0000000
## 1334 2015-08-27     Biden        NaN
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## 1337 2015-08-30     Biden 19.0000000
## 1338 2015-08-31     Biden 20.0000000
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## 1340 2015-09-02     Biden 19.5000000
## 1341 2015-09-03     Biden        NaN
## 1342 2015-09-04     Biden 20.0000000
## 1343 2015-09-05     Biden 18.3333333
## 1344 2015-09-06     Biden 18.3333333
## 1345 2015-09-07     Biden 19.0000000
## 1346 2015-09-08     Biden 19.0000000
## 1347 2015-09-09     Biden 17.5000000
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## 1352 2015-09-14     Biden 20.0000000
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## 1354 2015-09-16     Biden 18.6666667
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## 1371 2015-10-03     Biden 19.5000000
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## 1374 2015-10-06     Biden 18.0000000
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## 1381 2015-10-13     Biden 13.0000000
## 1382 2015-10-14     Biden 14.6666667
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## 1459 2015-12-30     Biden        NaN
## 1460 2015-12-31     Biden        NaN
## 1461 2015-01-01    Chafee        NaN
## 1462 2015-01-02    Chafee        NaN
## 1463 2015-01-03    Chafee        NaN
## 1464 2015-01-04    Chafee        NaN
## 1465 2015-01-05    Chafee        NaN
## 1466 2015-01-06    Chafee        NaN
## 1467 2015-01-07    Chafee        NaN
## 1468 2015-01-08    Chafee        NaN
## 1469 2015-01-09    Chafee        NaN
## 1470 2015-01-10    Chafee        NaN
## 1471 2015-01-11    Chafee        NaN
## 1472 2015-01-12    Chafee        NaN
## 1473 2015-01-13    Chafee        NaN
## 1474 2015-01-14    Chafee        NaN
## 1475 2015-01-15    Chafee        NaN
## 1476 2015-01-16    Chafee        NaN
## 1477 2015-01-17    Chafee        NaN
## 1478 2015-01-18    Chafee        NaN
## 1479 2015-01-19    Chafee        NaN
## 1480 2015-01-20    Chafee        NaN
## 1481 2015-01-21    Chafee        NaN
## 1482 2015-01-22    Chafee        NaN
## 1483 2015-01-23    Chafee        NaN
## 1484 2015-01-24    Chafee        NaN
## 1485 2015-01-25    Chafee        NaN
## 1486 2015-01-26    Chafee        NaN
## 1487 2015-01-27    Chafee        NaN
## 1488 2015-01-28    Chafee        NaN
## 1489 2015-01-29    Chafee        NaN
## 1490 2015-01-30    Chafee        NaN
## 1491 2015-01-31    Chafee        NaN
## 1492 2015-02-01    Chafee        NaN
## 1493 2015-02-02    Chafee        NaN
## 1494 2015-02-03    Chafee        NaN
## 1495 2015-02-04    Chafee        NaN
## 1496 2015-02-05    Chafee        NaN
## 1497 2015-02-06    Chafee        NaN
## 1498 2015-02-07    Chafee        NaN
## 1499 2015-02-08    Chafee        NaN
## 1500 2015-02-09    Chafee        NaN
## 1501 2015-02-10    Chafee        NaN
## 1502 2015-02-11    Chafee        NaN
## 1503 2015-02-12    Chafee        NaN
## 1504 2015-02-13    Chafee        NaN
## 1505 2015-02-14    Chafee        NaN
## 1506 2015-02-15    Chafee        NaN
## 1507 2015-02-16    Chafee        NaN
## 1508 2015-02-17    Chafee        NaN
## 1509 2015-02-18    Chafee        NaN
## 1510 2015-02-19    Chafee        NaN
## 1511 2015-02-20    Chafee        NaN
## 1512 2015-02-21    Chafee        NaN
## 1513 2015-02-22    Chafee        NaN
## 1514 2015-02-23    Chafee        NaN
## 1515 2015-02-24    Chafee        NaN
## 1516 2015-02-25    Chafee        NaN
## 1517 2015-02-26    Chafee        NaN
## 1518 2015-02-27    Chafee        NaN
## 1519 2015-02-28    Chafee        NaN
## 1520 2015-03-01    Chafee        NaN
## 1521 2015-03-02    Chafee        NaN
## 1522 2015-03-03    Chafee        NaN
## 1523 2015-03-04    Chafee        NaN
## 1524 2015-03-05    Chafee        NaN
## 1525 2015-03-06    Chafee        NaN
## 1526 2015-03-07    Chafee        NaN
## 1527 2015-03-08    Chafee        NaN
## 1528 2015-03-09    Chafee        NaN
## 1529 2015-03-10    Chafee        NaN
## 1530 2015-03-11    Chafee        NaN
## 1531 2015-03-12    Chafee        NaN
## 1532 2015-03-13    Chafee        NaN
## 1533 2015-03-14    Chafee        NaN
## 1534 2015-03-15    Chafee        NaN
## 1535 2015-03-16    Chafee        NaN
## 1536 2015-03-17    Chafee        NaN
## 1537 2015-03-18    Chafee        NaN
## 1538 2015-03-19    Chafee        NaN
## 1539 2015-03-20    Chafee        NaN
## 1540 2015-03-21    Chafee        NaN
## 1541 2015-03-22    Chafee        NaN
## 1542 2015-03-23    Chafee        NaN
## 1543 2015-03-24    Chafee        NaN
## 1544 2015-03-25    Chafee        NaN
## 1545 2015-03-26    Chafee        NaN
## 1546 2015-03-27    Chafee        NaN
## 1547 2015-03-28    Chafee        NaN
## 1548 2015-03-29    Chafee        NaN
## 1549 2015-03-30    Chafee        NaN
## 1550 2015-03-31    Chafee        NaN
## 1551 2015-04-01    Chafee        NaN
## 1552 2015-04-02    Chafee        NaN
## 1553 2015-04-03    Chafee        NaN
## 1554 2015-04-04    Chafee        NaN
## 1555 2015-04-05    Chafee        NaN
## 1556 2015-04-06    Chafee        NaN
## 1557 2015-04-07    Chafee        NaN
## 1558 2015-04-08    Chafee        NaN
## 1559 2015-04-09    Chafee        NaN
## 1560 2015-04-10    Chafee        NaN
## 1561 2015-04-11    Chafee        NaN
## 1562 2015-04-12    Chafee        NaN
## 1563 2015-04-13    Chafee        NaN
## 1564 2015-04-14    Chafee        NaN
## 1565 2015-04-15    Chafee        NaN
## 1566 2015-04-16    Chafee  0.5000000
## 1567 2015-04-17    Chafee  0.5000000
## 1568 2015-04-18    Chafee  0.5000000
## 1569 2015-04-19    Chafee  0.3333333
## 1570 2015-04-20    Chafee  0.0000000
## 1571 2015-04-21    Chafee  0.0000000
## 1572 2015-04-22    Chafee        NaN
## 1573 2015-04-23    Chafee        NaN
## 1574 2015-04-24    Chafee        NaN
## 1575 2015-04-25    Chafee        NaN
## 1576 2015-04-26    Chafee        NaN
## 1577 2015-04-27    Chafee        NaN
## 1578 2015-04-28    Chafee        NaN
## 1579 2015-04-29    Chafee        NaN
## 1580 2015-04-30    Chafee        NaN
## 1581 2015-05-01    Chafee        NaN
## 1582 2015-05-02    Chafee        NaN
## 1583 2015-05-03    Chafee        NaN
## 1584 2015-05-04    Chafee        NaN
## 1585 2015-05-05    Chafee        NaN
## 1586 2015-05-06    Chafee        NaN
## 1587 2015-05-07    Chafee  5.0000000
## 1588 2015-05-08    Chafee  5.0000000
## 1589 2015-05-09    Chafee  2.5000000
## 1590 2015-05-10    Chafee  2.5000000
## 1591 2015-05-11    Chafee  0.0000000
## 1592 2015-05-12    Chafee  0.0000000
## 1593 2015-05-13    Chafee        NaN
## 1594 2015-05-14    Chafee        NaN
## 1595 2015-05-15    Chafee        NaN
## 1596 2015-05-16    Chafee        NaN
## 1597 2015-05-17    Chafee        NaN
## 1598 2015-05-18    Chafee        NaN
## 1599 2015-05-19    Chafee  1.0000000
## 1600 2015-05-20    Chafee  1.0000000
## 1601 2015-05-21    Chafee  1.0000000
## 1602 2015-05-22    Chafee  1.0000000
## 1603 2015-05-23    Chafee  0.5000000
## 1604 2015-05-24    Chafee  0.5000000
## 1605 2015-05-25    Chafee  0.5000000
## 1606 2015-05-26    Chafee  1.0000000
## 1607 2015-05-27    Chafee        NaN
## 1608 2015-05-28    Chafee  1.0000000
## 1609 2015-05-29    Chafee  0.5000000
## 1610 2015-05-30    Chafee  0.6666667
## 1611 2015-05-31    Chafee  0.7500000
## 1612 2015-06-01    Chafee  1.0000000
## 1613 2015-06-02    Chafee  1.0000000
## 1614 2015-06-03    Chafee        NaN
## 1615 2015-06-04    Chafee        NaN
## 1616 2015-06-05    Chafee        NaN
## 1617 2015-06-06    Chafee  2.0000000
## 1618 2015-06-07    Chafee  2.0000000
## 1619 2015-06-08    Chafee  2.0000000
## 1620 2015-06-09    Chafee        NaN
## 1621 2015-06-10    Chafee        NaN
## 1622 2015-06-11    Chafee  2.0000000
## 1623 2015-06-12    Chafee  2.0000000
## 1624 2015-06-13    Chafee  1.6666667
## 1625 2015-06-14    Chafee  1.2500000
## 1626 2015-06-15    Chafee  0.5000000
## 1627 2015-06-16    Chafee  0.0000000
## 1628 2015-06-17    Chafee  0.0000000
## 1629 2015-06-18    Chafee  0.0000000
## 1630 2015-06-19    Chafee        NaN
## 1631 2015-06-20    Chafee  0.0000000
## 1632 2015-06-21    Chafee  0.0000000
## 1633 2015-06-22    Chafee  0.0000000
## 1634 2015-06-23    Chafee  0.0000000
## 1635 2015-06-24    Chafee        NaN
## 1636 2015-06-25    Chafee        NaN
## 1637 2015-06-26    Chafee  0.0000000
## 1638 2015-06-27    Chafee  0.0000000
## 1639 2015-06-28    Chafee  0.0000000
## 1640 2015-06-29    Chafee  0.0000000
## 1641 2015-06-30    Chafee        NaN
## 1642 2015-07-01    Chafee        NaN
## 1643 2015-07-02    Chafee        NaN
## 1644 2015-07-03    Chafee        NaN
## 1645 2015-07-04    Chafee  0.0000000
## 1646 2015-07-05    Chafee  0.0000000
## 1647 2015-07-06    Chafee  0.0000000
## 1648 2015-07-07    Chafee        NaN
## 1649 2015-07-08    Chafee        NaN
## 1650 2015-07-09    Chafee  0.0000000
## 1651 2015-07-10    Chafee  0.0000000
## 1652 2015-07-11    Chafee  0.0000000
## 1653 2015-07-12    Chafee  0.0000000
## 1654 2015-07-13    Chafee  0.5000000
## 1655 2015-07-14    Chafee  0.5000000
## 1656 2015-07-15    Chafee  1.0000000
## 1657 2015-07-16    Chafee  0.0000000
## 1658 2015-07-17    Chafee  0.0000000
## 1659 2015-07-18    Chafee  0.5000000
## 1660 2015-07-19    Chafee  0.5000000
## 1661 2015-07-20    Chafee  2.0000000
## 1662 2015-07-21    Chafee  3.0000000
## 1663 2015-07-22    Chafee  0.0000000
## 1664 2015-07-23    Chafee  0.0000000
## 1665 2015-07-24    Chafee  0.0000000
## 1666 2015-07-25    Chafee  0.0000000
## 1667 2015-07-26    Chafee  0.5000000
## 1668 2015-07-27    Chafee  0.5000000
## 1669 2015-07-28    Chafee  0.5000000
## 1670 2015-07-29    Chafee  1.0000000
## 1671 2015-07-30    Chafee  0.6666667
## 1672 2015-07-31    Chafee  0.7500000
## 1673 2015-08-01    Chafee  0.8000000
## 1674 2015-08-02    Chafee  0.8000000
## 1675 2015-08-03    Chafee  1.0000000
## 1676 2015-08-04    Chafee  1.0000000
## 1677 2015-08-05    Chafee  1.0000000
## 1678 2015-08-06    Chafee        NaN
## 1679 2015-08-07    Chafee  1.0000000
## 1680 2015-08-08    Chafee  0.5000000
## 1681 2015-08-09    Chafee  0.5000000
## 1682 2015-08-10    Chafee  0.0000000
## 1683 2015-08-11    Chafee  0.0000000
## 1684 2015-08-12    Chafee  0.0000000
## 1685 2015-08-13    Chafee  0.0000000
## 1686 2015-08-14    Chafee  0.6666667
## 1687 2015-08-15    Chafee  0.7500000
## 1688 2015-08-16    Chafee  0.7500000
## 1689 2015-08-17    Chafee  1.0000000
## 1690 2015-08-18    Chafee  1.0000000
## 1691 2015-08-19    Chafee  1.0000000
## 1692 2015-08-20    Chafee  0.0000000
## 1693 2015-08-21    Chafee  0.5000000
## 1694 2015-08-22    Chafee  0.3333333
## 1695 2015-08-23    Chafee  0.0000000
## 1696 2015-08-24    Chafee  0.0000000
## 1697 2015-08-25    Chafee  0.0000000
## 1698 2015-08-26    Chafee  0.0000000
## 1699 2015-08-27    Chafee        NaN
## 1700 2015-08-28    Chafee  0.6666667
## 1701 2015-08-29    Chafee  0.7500000
## 1702 2015-08-30    Chafee  0.7500000
## 1703 2015-08-31    Chafee  0.3333333
## 1704 2015-09-01    Chafee  0.3333333
## 1705 2015-09-02    Chafee  0.5000000
## 1706 2015-09-03    Chafee        NaN
## 1707 2015-09-04    Chafee  0.5000000
## 1708 2015-09-05    Chafee  0.6666667
## 1709 2015-09-06    Chafee  0.6666667
## 1710 2015-09-07    Chafee  0.7500000
## 1711 2015-09-08    Chafee  0.6666667
## 1712 2015-09-09    Chafee  1.0000000
## 1713 2015-09-10    Chafee  1.0000000
## 1714 2015-09-11    Chafee  0.5000000
## 1715 2015-09-12    Chafee  0.3333333
## 1716 2015-09-13    Chafee  0.3333333
## 1717 2015-09-14    Chafee  0.5000000
## 1718 2015-09-15    Chafee  0.5000000
## 1719 2015-09-16    Chafee  0.0000000
## 1720 2015-09-17    Chafee  0.0000000
## 1721 2015-09-18    Chafee  0.2000000
## 1722 2015-09-19    Chafee  0.2000000
## 1723 2015-09-20    Chafee  0.1666667
## 1724 2015-09-21    Chafee  0.0000000
## 1725 2015-09-22    Chafee  0.0000000
## 1726 2015-09-23    Chafee  0.0000000
## 1727 2015-09-24    Chafee  0.3333333
## 1728 2015-09-25    Chafee  0.3333333
## 1729 2015-09-26    Chafee  0.5000000
## 1730 2015-09-27    Chafee  0.5000000
## 1731 2015-09-28    Chafee  0.6666667
## 1732 2015-09-29    Chafee  0.5000000
## 1733 2015-09-30    Chafee  1.5000000
## 1734 2015-10-01    Chafee  1.0000000
## 1735 2015-10-02    Chafee  1.0000000
## 1736 2015-10-03    Chafee  1.0000000
## 1737 2015-10-04    Chafee  1.0000000
## 1738 2015-10-05    Chafee  1.0000000
## 1739 2015-10-06    Chafee  1.0000000
## 1740 2015-10-07    Chafee  1.0000000
## 1741 2015-10-08    Chafee  1.0000000
## 1742 2015-10-09    Chafee  1.0000000
## 1743 2015-10-10    Chafee  0.7500000
## 1744 2015-10-11    Chafee  0.7500000
## 1745 2015-10-12    Chafee  0.7500000
## 1746 2015-10-13    Chafee  0.5000000
## 1747 2015-10-14    Chafee  0.3333333
## 1748 2015-10-15    Chafee  0.1666667
## 1749 2015-10-16    Chafee  0.1666667
## 1750 2015-10-17    Chafee  0.2857143
## 1751 2015-10-18    Chafee  0.4000000
## 1752 2015-10-19    Chafee  1.0000000
## 1753 2015-10-20    Chafee  1.0000000
## 1754 2015-10-21    Chafee  1.0000000
## 1755 2015-10-22    Chafee        NaN
## 1756 2015-10-23    Chafee        NaN
## 1757 2015-10-24    Chafee        NaN
## 1758 2015-10-25    Chafee        NaN
## 1759 2015-10-26    Chafee        NaN
## 1760 2015-10-27    Chafee        NaN
## 1761 2015-10-28    Chafee        NaN
## 1762 2015-10-29    Chafee        NaN
## 1763 2015-10-30    Chafee        NaN
## 1764 2015-10-31    Chafee        NaN
## 1765 2015-11-01    Chafee        NaN
## 1766 2015-11-02    Chafee        NaN
## 1767 2015-11-03    Chafee        NaN
## 1768 2015-11-04    Chafee        NaN
## 1769 2015-11-05    Chafee        NaN
## 1770 2015-11-06    Chafee        NaN
## 1771 2015-11-07    Chafee        NaN
## 1772 2015-11-08    Chafee        NaN
## 1773 2015-11-09    Chafee        NaN
## 1774 2015-11-10    Chafee        NaN
## 1775 2015-11-11    Chafee        NaN
## 1776 2015-11-12    Chafee        NaN
## 1777 2015-11-13    Chafee        NaN
## 1778 2015-11-14    Chafee        NaN
## 1779 2015-11-15    Chafee        NaN
## 1780 2015-11-16    Chafee        NaN
## 1781 2015-11-17    Chafee        NaN
## 1782 2015-11-18    Chafee        NaN
## 1783 2015-11-19    Chafee        NaN
## 1784 2015-11-20    Chafee        NaN
## 1785 2015-11-21    Chafee        NaN
## 1786 2015-11-22    Chafee        NaN
## 1787 2015-11-23    Chafee        NaN
## 1788 2015-11-24    Chafee        NaN
## 1789 2015-11-25    Chafee        NaN
## 1790 2015-11-26    Chafee        NaN
## 1791 2015-11-27    Chafee        NaN
## 1792 2015-11-28    Chafee        NaN
## 1793 2015-11-29    Chafee        NaN
## 1794 2015-11-30    Chafee        NaN
## 1795 2015-12-01    Chafee        NaN
## 1796 2015-12-02    Chafee        NaN
## 1797 2015-12-03    Chafee        NaN
## 1798 2015-12-04    Chafee        NaN
## 1799 2015-12-05    Chafee        NaN
## 1800 2015-12-06    Chafee        NaN
## 1801 2015-12-07    Chafee        NaN
## 1802 2015-12-08    Chafee        NaN
## 1803 2015-12-09    Chafee        NaN
## 1804 2015-12-10    Chafee        NaN
## 1805 2015-12-11    Chafee        NaN
## 1806 2015-12-12    Chafee        NaN
## 1807 2015-12-13    Chafee        NaN
## 1808 2015-12-14    Chafee        NaN
## 1809 2015-12-15    Chafee        NaN
## 1810 2015-12-16    Chafee        NaN
## 1811 2015-12-17    Chafee        NaN
## 1812 2015-12-18    Chafee        NaN
## 1813 2015-12-19    Chafee        NaN
## 1814 2015-12-20    Chafee        NaN
## 1815 2015-12-21    Chafee        NaN
## 1816 2015-12-22    Chafee        NaN
## 1817 2015-12-23    Chafee        NaN
## 1818 2015-12-24    Chafee        NaN
## 1819 2015-12-25    Chafee        NaN
## 1820 2015-12-26    Chafee        NaN
## 1821 2015-12-27    Chafee        NaN
## 1822 2015-12-28    Chafee        NaN
## 1823 2015-12-29    Chafee        NaN
## 1824 2015-12-30    Chafee        NaN
## 1825 2015-12-31    Chafee        NaN
## 1826 2015-01-01    Lessig        NaN
## 1827 2015-01-02    Lessig        NaN
## 1828 2015-01-03    Lessig        NaN
## 1829 2015-01-04    Lessig        NaN
## 1830 2015-01-05    Lessig        NaN
## 1831 2015-01-06    Lessig        NaN
## 1832 2015-01-07    Lessig        NaN
## 1833 2015-01-08    Lessig        NaN
## 1834 2015-01-09    Lessig        NaN
## 1835 2015-01-10    Lessig        NaN
## 1836 2015-01-11    Lessig        NaN
## 1837 2015-01-12    Lessig        NaN
## 1838 2015-01-13    Lessig        NaN
## 1839 2015-01-14    Lessig        NaN
## 1840 2015-01-15    Lessig        NaN
## 1841 2015-01-16    Lessig        NaN
## 1842 2015-01-17    Lessig        NaN
## 1843 2015-01-18    Lessig        NaN
## 1844 2015-01-19    Lessig        NaN
## 1845 2015-01-20    Lessig        NaN
## 1846 2015-01-21    Lessig        NaN
## 1847 2015-01-22    Lessig        NaN
## 1848 2015-01-23    Lessig        NaN
## 1849 2015-01-24    Lessig        NaN
## 1850 2015-01-25    Lessig        NaN
## 1851 2015-01-26    Lessig        NaN
## 1852 2015-01-27    Lessig        NaN
## 1853 2015-01-28    Lessig        NaN
## 1854 2015-01-29    Lessig        NaN
## 1855 2015-01-30    Lessig        NaN
## 1856 2015-01-31    Lessig        NaN
## 1857 2015-02-01    Lessig        NaN
## 1858 2015-02-02    Lessig        NaN
## 1859 2015-02-03    Lessig        NaN
## 1860 2015-02-04    Lessig        NaN
## 1861 2015-02-05    Lessig        NaN
## 1862 2015-02-06    Lessig        NaN
## 1863 2015-02-07    Lessig        NaN
## 1864 2015-02-08    Lessig        NaN
## 1865 2015-02-09    Lessig        NaN
## 1866 2015-02-10    Lessig        NaN
## 1867 2015-02-11    Lessig        NaN
## 1868 2015-02-12    Lessig        NaN
## 1869 2015-02-13    Lessig        NaN
## 1870 2015-02-14    Lessig        NaN
## 1871 2015-02-15    Lessig        NaN
## 1872 2015-02-16    Lessig        NaN
## 1873 2015-02-17    Lessig        NaN
## 1874 2015-02-18    Lessig        NaN
## 1875 2015-02-19    Lessig        NaN
## 1876 2015-02-20    Lessig        NaN
## 1877 2015-02-21    Lessig        NaN
## 1878 2015-02-22    Lessig        NaN
## 1879 2015-02-23    Lessig        NaN
## 1880 2015-02-24    Lessig        NaN
## 1881 2015-02-25    Lessig        NaN
## 1882 2015-02-26    Lessig        NaN
## 1883 2015-02-27    Lessig        NaN
## 1884 2015-02-28    Lessig        NaN
## 1885 2015-03-01    Lessig        NaN
## 1886 2015-03-02    Lessig        NaN
## 1887 2015-03-03    Lessig        NaN
## 1888 2015-03-04    Lessig        NaN
## 1889 2015-03-05    Lessig        NaN
## 1890 2015-03-06    Lessig        NaN
## 1891 2015-03-07    Lessig        NaN
## 1892 2015-03-08    Lessig        NaN
## 1893 2015-03-09    Lessig        NaN
## 1894 2015-03-10    Lessig        NaN
## 1895 2015-03-11    Lessig        NaN
## 1896 2015-03-12    Lessig        NaN
## 1897 2015-03-13    Lessig        NaN
## 1898 2015-03-14    Lessig        NaN
## 1899 2015-03-15    Lessig        NaN
## 1900 2015-03-16    Lessig        NaN
## 1901 2015-03-17    Lessig        NaN
## 1902 2015-03-18    Lessig        NaN
## 1903 2015-03-19    Lessig        NaN
## 1904 2015-03-20    Lessig        NaN
## 1905 2015-03-21    Lessig        NaN
## 1906 2015-03-22    Lessig        NaN
## 1907 2015-03-23    Lessig        NaN
## 1908 2015-03-24    Lessig        NaN
## 1909 2015-03-25    Lessig        NaN
## 1910 2015-03-26    Lessig        NaN
## 1911 2015-03-27    Lessig        NaN
## 1912 2015-03-28    Lessig        NaN
## 1913 2015-03-29    Lessig        NaN
## 1914 2015-03-30    Lessig        NaN
## 1915 2015-03-31    Lessig        NaN
## 1916 2015-04-01    Lessig        NaN
## 1917 2015-04-02    Lessig        NaN
## 1918 2015-04-03    Lessig        NaN
## 1919 2015-04-04    Lessig        NaN
## 1920 2015-04-05    Lessig        NaN
## 1921 2015-04-06    Lessig        NaN
## 1922 2015-04-07    Lessig        NaN
## 1923 2015-04-08    Lessig        NaN
## 1924 2015-04-09    Lessig        NaN
## 1925 2015-04-10    Lessig        NaN
## 1926 2015-04-11    Lessig        NaN
## 1927 2015-04-12    Lessig        NaN
## 1928 2015-04-13    Lessig        NaN
## 1929 2015-04-14    Lessig        NaN
## 1930 2015-04-15    Lessig        NaN
## 1931 2015-04-16    Lessig        NaN
## 1932 2015-04-17    Lessig        NaN
## 1933 2015-04-18    Lessig        NaN
## 1934 2015-04-19    Lessig        NaN
## 1935 2015-04-20    Lessig        NaN
## 1936 2015-04-21    Lessig        NaN
## 1937 2015-04-22    Lessig        NaN
## 1938 2015-04-23    Lessig        NaN
## 1939 2015-04-24    Lessig        NaN
## 1940 2015-04-25    Lessig        NaN
## 1941 2015-04-26    Lessig        NaN
## 1942 2015-04-27    Lessig        NaN
## 1943 2015-04-28    Lessig        NaN
## 1944 2015-04-29    Lessig        NaN
## 1945 2015-04-30    Lessig        NaN
## 1946 2015-05-01    Lessig        NaN
## 1947 2015-05-02    Lessig        NaN
## 1948 2015-05-03    Lessig        NaN
## 1949 2015-05-04    Lessig        NaN
## 1950 2015-05-05    Lessig        NaN
## 1951 2015-05-06    Lessig        NaN
## 1952 2015-05-07    Lessig        NaN
## 1953 2015-05-08    Lessig        NaN
## 1954 2015-05-09    Lessig        NaN
## 1955 2015-05-10    Lessig        NaN
## 1956 2015-05-11    Lessig        NaN
## 1957 2015-05-12    Lessig        NaN
## 1958 2015-05-13    Lessig        NaN
## 1959 2015-05-14    Lessig        NaN
## 1960 2015-05-15    Lessig        NaN
## 1961 2015-05-16    Lessig        NaN
## 1962 2015-05-17    Lessig        NaN
## 1963 2015-05-18    Lessig        NaN
## 1964 2015-05-19    Lessig        NaN
## 1965 2015-05-20    Lessig        NaN
## 1966 2015-05-21    Lessig        NaN
## 1967 2015-05-22    Lessig        NaN
## 1968 2015-05-23    Lessig        NaN
## 1969 2015-05-24    Lessig        NaN
## 1970 2015-05-25    Lessig        NaN
## 1971 2015-05-26    Lessig        NaN
## 1972 2015-05-27    Lessig        NaN
## 1973 2015-05-28    Lessig        NaN
## 1974 2015-05-29    Lessig        NaN
## 1975 2015-05-30    Lessig        NaN
## 1976 2015-05-31    Lessig        NaN
## 1977 2015-06-01    Lessig        NaN
## 1978 2015-06-02    Lessig        NaN
## 1979 2015-06-03    Lessig        NaN
## 1980 2015-06-04    Lessig        NaN
## 1981 2015-06-05    Lessig        NaN
## 1982 2015-06-06    Lessig        NaN
## 1983 2015-06-07    Lessig        NaN
## 1984 2015-06-08    Lessig        NaN
## 1985 2015-06-09    Lessig        NaN
## 1986 2015-06-10    Lessig        NaN
## 1987 2015-06-11    Lessig        NaN
## 1988 2015-06-12    Lessig        NaN
## 1989 2015-06-13    Lessig        NaN
## 1990 2015-06-14    Lessig        NaN
## 1991 2015-06-15    Lessig        NaN
## 1992 2015-06-16    Lessig        NaN
## 1993 2015-06-17    Lessig        NaN
## 1994 2015-06-18    Lessig        NaN
## 1995 2015-06-19    Lessig        NaN
## 1996 2015-06-20    Lessig        NaN
## 1997 2015-06-21    Lessig        NaN
## 1998 2015-06-22    Lessig        NaN
## 1999 2015-06-23    Lessig        NaN
## 2000 2015-06-24    Lessig        NaN
## 2001 2015-06-25    Lessig        NaN
## 2002 2015-06-26    Lessig        NaN
## 2003 2015-06-27    Lessig        NaN
## 2004 2015-06-28    Lessig        NaN
## 2005 2015-06-29    Lessig        NaN
## 2006 2015-06-30    Lessig        NaN
## 2007 2015-07-01    Lessig        NaN
## 2008 2015-07-02    Lessig        NaN
## 2009 2015-07-03    Lessig        NaN
## 2010 2015-07-04    Lessig        NaN
## 2011 2015-07-05    Lessig        NaN
## 2012 2015-07-06    Lessig        NaN
## 2013 2015-07-07    Lessig        NaN
## 2014 2015-07-08    Lessig        NaN
## 2015 2015-07-09    Lessig        NaN
## 2016 2015-07-10    Lessig        NaN
## 2017 2015-07-11    Lessig        NaN
## 2018 2015-07-12    Lessig        NaN
## 2019 2015-07-13    Lessig        NaN
## 2020 2015-07-14    Lessig        NaN
## 2021 2015-07-15    Lessig        NaN
## 2022 2015-07-16    Lessig        NaN
## 2023 2015-07-17    Lessig        NaN
## 2024 2015-07-18    Lessig        NaN
## 2025 2015-07-19    Lessig        NaN
## 2026 2015-07-20    Lessig        NaN
## 2027 2015-07-21    Lessig        NaN
## 2028 2015-07-22    Lessig        NaN
## 2029 2015-07-23    Lessig        NaN
## 2030 2015-07-24    Lessig        NaN
## 2031 2015-07-25    Lessig        NaN
## 2032 2015-07-26    Lessig        NaN
## 2033 2015-07-27    Lessig        NaN
## 2034 2015-07-28    Lessig        NaN
## 2035 2015-07-29    Lessig        NaN
## 2036 2015-07-30    Lessig        NaN
## 2037 2015-07-31    Lessig        NaN
## 2038 2015-08-01    Lessig        NaN
## 2039 2015-08-02    Lessig        NaN
## 2040 2015-08-03    Lessig        NaN
## 2041 2015-08-04    Lessig        NaN
## 2042 2015-08-05    Lessig        NaN
## 2043 2015-08-06    Lessig        NaN
## 2044 2015-08-07    Lessig        NaN
## 2045 2015-08-08    Lessig        NaN
## 2046 2015-08-09    Lessig        NaN
## 2047 2015-08-10    Lessig        NaN
## 2048 2015-08-11    Lessig        NaN
## 2049 2015-08-12    Lessig        NaN
## 2050 2015-08-13    Lessig        NaN
## 2051 2015-08-14    Lessig        NaN
## 2052 2015-08-15    Lessig        NaN
## 2053 2015-08-16    Lessig        NaN
## 2054 2015-08-17    Lessig        NaN
## 2055 2015-08-18    Lessig        NaN
## 2056 2015-08-19    Lessig        NaN
## 2057 2015-08-20    Lessig        NaN
## 2058 2015-08-21    Lessig        NaN
## 2059 2015-08-22    Lessig        NaN
## 2060 2015-08-23    Lessig        NaN
## 2061 2015-08-24    Lessig        NaN
## 2062 2015-08-25    Lessig        NaN
## 2063 2015-08-26    Lessig        NaN
## 2064 2015-08-27    Lessig        NaN
## 2065 2015-08-28    Lessig  1.0000000
## 2066 2015-08-29    Lessig  1.0000000
## 2067 2015-08-30    Lessig  1.0000000
## 2068 2015-08-31    Lessig        NaN
## 2069 2015-09-01    Lessig        NaN
## 2070 2015-09-02    Lessig        NaN
## 2071 2015-09-03    Lessig        NaN
## 2072 2015-09-04    Lessig        NaN
## 2073 2015-09-05    Lessig        NaN
## 2074 2015-09-06    Lessig        NaN
## 2075 2015-09-07    Lessig        NaN
## 2076 2015-09-08    Lessig        NaN
## 2077 2015-09-09    Lessig        NaN
## 2078 2015-09-10    Lessig        NaN
## 2079 2015-09-11    Lessig        NaN
## 2080 2015-09-12    Lessig        NaN
## 2081 2015-09-13    Lessig        NaN
## 2082 2015-09-14    Lessig        NaN
## 2083 2015-09-15    Lessig        NaN
## 2084 2015-09-16    Lessig  0.0000000
## 2085 2015-09-17    Lessig  0.0000000
## 2086 2015-09-18    Lessig  0.0000000
## 2087 2015-09-19    Lessig  0.0000000
## 2088 2015-09-20    Lessig  0.0000000
## 2089 2015-09-21    Lessig  0.0000000
## 2090 2015-09-22    Lessig        NaN
## 2091 2015-09-23    Lessig        NaN
## 2092 2015-09-24    Lessig  0.0000000
## 2093 2015-09-25    Lessig  0.0000000
## 2094 2015-09-26    Lessig  0.0000000
## 2095 2015-09-27    Lessig  0.0000000
## 2096 2015-09-28    Lessig  0.0000000
## 2097 2015-09-29    Lessig        NaN
## 2098 2015-09-30    Lessig        NaN
## 2099 2015-10-01    Lessig  0.0000000
## 2100 2015-10-02    Lessig  0.0000000
## 2101 2015-10-03    Lessig  0.0000000
## 2102 2015-10-04    Lessig  0.0000000
## 2103 2015-10-05    Lessig  0.0000000
## 2104 2015-10-06    Lessig        NaN
## 2105 2015-10-07    Lessig        NaN
## 2106 2015-10-08    Lessig  1.0000000
## 2107 2015-10-09    Lessig  1.0000000
## 2108 2015-10-10    Lessig  0.5000000
## 2109 2015-10-11    Lessig  0.5000000
## 2110 2015-10-12    Lessig  0.5000000
## 2111 2015-10-13    Lessig  0.0000000
## 2112 2015-10-14    Lessig  0.0000000
## 2113 2015-10-15    Lessig  0.3333333
## 2114 2015-10-16    Lessig  0.5000000
## 2115 2015-10-17    Lessig  0.5000000
## 2116 2015-10-18    Lessig  1.0000000
## 2117 2015-10-19    Lessig        NaN
## 2118 2015-10-20    Lessig        NaN
## 2119 2015-10-21    Lessig        NaN
## 2120 2015-10-22    Lessig        NaN
## 2121 2015-10-23    Lessig  1.0000000
## 2122 2015-10-24    Lessig  1.0000000
## 2123 2015-10-25    Lessig  1.0000000
## 2124 2015-10-26    Lessig  1.0000000
## 2125 2015-10-27    Lessig  1.0000000
## 2126 2015-10-28    Lessig  1.0000000
## 2127 2015-10-29    Lessig  1.0000000
## 2128 2015-10-30    Lessig  1.0000000
## 2129 2015-10-31    Lessig  1.0000000
## 2130 2015-11-01    Lessig  1.0000000
## 2131 2015-11-02    Lessig  1.0000000
## 2132 2015-11-03    Lessig  1.0000000
## 2133 2015-11-04    Lessig        NaN
## 2134 2015-11-05    Lessig  0.0000000
## 2135 2015-11-06    Lessig  0.0000000
## 2136 2015-11-07    Lessig  0.0000000
## 2137 2015-11-08    Lessig  0.0000000
## 2138 2015-11-09    Lessig  0.0000000
## 2139 2015-11-10    Lessig        NaN
## 2140 2015-11-11    Lessig        NaN
## 2141 2015-11-12    Lessig        NaN
## 2142 2015-11-13    Lessig        NaN
## 2143 2015-11-14    Lessig        NaN
## 2144 2015-11-15    Lessig        NaN
## 2145 2015-11-16    Lessig        NaN
## 2146 2015-11-17    Lessig        NaN
## 2147 2015-11-18    Lessig        NaN
## 2148 2015-11-19    Lessig        NaN
## 2149 2015-11-20    Lessig        NaN
## 2150 2015-11-21    Lessig        NaN
## 2151 2015-11-22    Lessig        NaN
## 2152 2015-11-23    Lessig        NaN
## 2153 2015-11-24    Lessig        NaN
## 2154 2015-11-25    Lessig        NaN
## 2155 2015-11-26    Lessig        NaN
## 2156 2015-11-27    Lessig        NaN
## 2157 2015-11-28    Lessig        NaN
## 2158 2015-11-29    Lessig        NaN
## 2159 2015-11-30    Lessig        NaN
## 2160 2015-12-01    Lessig        NaN
## 2161 2015-12-02    Lessig        NaN
## 2162 2015-12-03    Lessig        NaN
## 2163 2015-12-04    Lessig        NaN
## 2164 2015-12-05    Lessig        NaN
## 2165 2015-12-06    Lessig        NaN
## 2166 2015-12-07    Lessig        NaN
## 2167 2015-12-08    Lessig        NaN
## 2168 2015-12-09    Lessig        NaN
## 2169 2015-12-10    Lessig        NaN
## 2170 2015-12-11    Lessig        NaN
## 2171 2015-12-12    Lessig        NaN
## 2172 2015-12-13    Lessig        NaN
## 2173 2015-12-14    Lessig        NaN
## 2174 2015-12-15    Lessig        NaN
## 2175 2015-12-16    Lessig        NaN
## 2176 2015-12-17    Lessig        NaN
## 2177 2015-12-18    Lessig        NaN
## 2178 2015-12-19    Lessig        NaN
## 2179 2015-12-20    Lessig        NaN
## 2180 2015-12-21    Lessig        NaN
## 2181 2015-12-22    Lessig        NaN
## 2182 2015-12-23    Lessig        NaN
## 2183 2015-12-24    Lessig        NaN
## 2184 2015-12-25    Lessig        NaN
## 2185 2015-12-26    Lessig        NaN
## 2186 2015-12-27    Lessig        NaN
## 2187 2015-12-28    Lessig        NaN
## 2188 2015-12-29    Lessig        NaN
## 2189 2015-12-30    Lessig        NaN
## 2190 2015-12-31    Lessig        NaN
## 2191 2015-01-01      Webb        NaN
## 2192 2015-01-02      Webb        NaN
## 2193 2015-01-03      Webb        NaN
## 2194 2015-01-04      Webb        NaN
## 2195 2015-01-05      Webb        NaN
## 2196 2015-01-06      Webb        NaN
## 2197 2015-01-07      Webb        NaN
## 2198 2015-01-08      Webb        NaN
## 2199 2015-01-09      Webb        NaN
## 2200 2015-01-10      Webb  2.0000000
## 2201 2015-01-11      Webb  2.0000000
## 2202 2015-01-12      Webb  2.0000000
## 2203 2015-01-13      Webb        NaN
## 2204 2015-01-14      Webb        NaN
## 2205 2015-01-15      Webb        NaN
## 2206 2015-01-16      Webb        NaN
## 2207 2015-01-17      Webb        NaN
## 2208 2015-01-18      Webb        NaN
## 2209 2015-01-19      Webb        NaN
## 2210 2015-01-20      Webb        NaN
## 2211 2015-01-21      Webb        NaN
## 2212 2015-01-22      Webb  1.0000000
## 2213 2015-01-23      Webb  1.0000000
## 2214 2015-01-24      Webb  1.0000000
## 2215 2015-01-25      Webb  1.0000000
## 2216 2015-01-26      Webb  1.0000000
## 2217 2015-01-27      Webb  1.0000000
## 2218 2015-01-28      Webb        NaN
## 2219 2015-01-29      Webb        NaN
## 2220 2015-01-30      Webb        NaN
## 2221 2015-01-31      Webb        NaN
## 2222 2015-02-01      Webb        NaN
## 2223 2015-02-02      Webb        NaN
## 2224 2015-02-03      Webb        NaN
## 2225 2015-02-04      Webb        NaN
## 2226 2015-02-05      Webb        NaN
## 2227 2015-02-06      Webb        NaN
## 2228 2015-02-07      Webb        NaN
## 2229 2015-02-08      Webb        NaN
## 2230 2015-02-09      Webb        NaN
## 2231 2015-02-10      Webb        NaN
## 2232 2015-02-11      Webb        NaN
## 2233 2015-02-12      Webb  2.0000000
## 2234 2015-02-13      Webb  2.0000000
## 2235 2015-02-14      Webb  2.0000000
## 2236 2015-02-15      Webb  2.0000000
## 2237 2015-02-16      Webb        NaN
## 2238 2015-02-17      Webb        NaN
## 2239 2015-02-18      Webb        NaN
## 2240 2015-02-19      Webb        NaN
## 2241 2015-02-20      Webb  2.0000000
## 2242 2015-02-21      Webb  2.0000000
## 2243 2015-02-22      Webb  2.0000000
## 2244 2015-02-23      Webb  2.0000000
## 2245 2015-02-24      Webb        NaN
## 2246 2015-02-25      Webb        NaN
## 2247 2015-02-26      Webb  1.0000000
## 2248 2015-02-27      Webb  1.0000000
## 2249 2015-02-28      Webb  1.0000000
## 2250 2015-03-01      Webb  1.0000000
## 2251 2015-03-02      Webb  1.0000000
## 2252 2015-03-03      Webb  1.0000000
## 2253 2015-03-04      Webb  1.0000000
## 2254 2015-03-05      Webb        NaN
## 2255 2015-03-06      Webb        NaN
## 2256 2015-03-07      Webb  0.0000000
## 2257 2015-03-08      Webb  0.0000000
## 2258 2015-03-09      Webb  0.0000000
## 2259 2015-03-10      Webb        NaN
## 2260 2015-03-11      Webb        NaN
## 2261 2015-03-12      Webb        NaN
## 2262 2015-03-13      Webb  1.0000000
## 2263 2015-03-14      Webb  1.0000000
## 2264 2015-03-15      Webb  1.0000000
## 2265 2015-03-16      Webb  1.0000000
## 2266 2015-03-17      Webb        NaN
## 2267 2015-03-18      Webb        NaN
## 2268 2015-03-19      Webb        NaN
## 2269 2015-03-20      Webb        NaN
## 2270 2015-03-21      Webb  1.0000000
## 2271 2015-03-22      Webb  1.0000000
## 2272 2015-03-23      Webb  1.0000000
## 2273 2015-03-24      Webb        NaN
## 2274 2015-03-25      Webb        NaN
## 2275 2015-03-26      Webb  1.5000000
## 2276 2015-03-27      Webb  1.5000000
## 2277 2015-03-28      Webb  1.5000000
## 2278 2015-03-29      Webb  1.3333333
## 2279 2015-03-30      Webb  1.3333333
## 2280 2015-03-31      Webb  1.3333333
## 2281 2015-04-01      Webb  1.0000000
## 2282 2015-04-02      Webb  1.0000000
## 2283 2015-04-03      Webb        NaN
## 2284 2015-04-04      Webb        NaN
## 2285 2015-04-05      Webb        NaN
## 2286 2015-04-06      Webb        NaN
## 2287 2015-04-07      Webb        NaN
## 2288 2015-04-08      Webb        NaN
## 2289 2015-04-09      Webb        NaN
## 2290 2015-04-10      Webb        NaN
## 2291 2015-04-11      Webb  0.0000000
## 2292 2015-04-12      Webb  0.0000000
## 2293 2015-04-13      Webb  0.0000000
## 2294 2015-04-14      Webb        NaN
## 2295 2015-04-15      Webb        NaN
## 2296 2015-04-16      Webb  2.0000000
## 2297 2015-04-17      Webb  2.0000000
## 2298 2015-04-18      Webb  2.0000000
## 2299 2015-04-19      Webb  1.5000000
## 2300 2015-04-20      Webb  1.0000000
## 2301 2015-04-21      Webb  0.5000000
## 2302 2015-04-22      Webb        NaN
## 2303 2015-04-23      Webb        NaN
## 2304 2015-04-24      Webb        NaN
## 2305 2015-04-25      Webb  1.0000000
## 2306 2015-04-26      Webb  1.0000000
## 2307 2015-04-27      Webb  1.0000000
## 2308 2015-04-28      Webb        NaN
## 2309 2015-04-29      Webb        NaN
## 2310 2015-04-30      Webb        NaN
## 2311 2015-05-01      Webb        NaN
## 2312 2015-05-02      Webb  0.0000000
## 2313 2015-05-03      Webb  0.0000000
## 2314 2015-05-04      Webb  0.0000000
## 2315 2015-05-05      Webb        NaN
## 2316 2015-05-06      Webb        NaN
## 2317 2015-05-07      Webb  6.0000000
## 2318 2015-05-08      Webb  6.0000000
## 2319 2015-05-09      Webb  3.0000000
## 2320 2015-05-10      Webb  3.0000000
## 2321 2015-05-11      Webb  1.5000000
## 2322 2015-05-12      Webb  2.0000000
## 2323 2015-05-13      Webb        NaN
## 2324 2015-05-14      Webb        NaN
## 2325 2015-05-15      Webb        NaN
## 2326 2015-05-16      Webb  3.0000000
## 2327 2015-05-17      Webb  3.0000000
## 2328 2015-05-18      Webb  3.0000000
## 2329 2015-05-19      Webb  1.0000000
## 2330 2015-05-20      Webb  1.0000000
## 2331 2015-05-21      Webb  1.0000000
## 2332 2015-05-22      Webb  1.0000000
## 2333 2015-05-23      Webb  1.0000000
## 2334 2015-05-24      Webb  1.0000000
## 2335 2015-05-25      Webb  1.0000000
## 2336 2015-05-26      Webb  1.0000000
## 2337 2015-05-27      Webb        NaN
## 2338 2015-05-28      Webb  1.0000000
## 2339 2015-05-29      Webb  1.5000000
## 2340 2015-05-30      Webb  1.0000000
## 2341 2015-05-31      Webb  1.2500000
## 2342 2015-06-01      Webb  1.0000000
## 2343 2015-06-02      Webb  2.0000000
## 2344 2015-06-03      Webb        NaN
## 2345 2015-06-04      Webb        NaN
## 2346 2015-06-05      Webb        NaN
## 2347 2015-06-06      Webb  0.0000000
## 2348 2015-06-07      Webb  0.0000000
## 2349 2015-06-08      Webb  0.0000000
## 2350 2015-06-09      Webb        NaN
## 2351 2015-06-10      Webb        NaN
## 2352 2015-06-11      Webb  3.0000000
## 2353 2015-06-12      Webb  3.0000000
## 2354 2015-06-13      Webb  2.3333333
## 2355 2015-06-14      Webb  2.7500000
## 2356 2015-06-15      Webb  2.5000000
## 2357 2015-06-16      Webb  4.0000000
## 2358 2015-06-17      Webb  4.0000000
## 2359 2015-06-18      Webb  4.0000000
## 2360 2015-06-19      Webb        NaN
## 2361 2015-06-20      Webb  0.0000000
## 2362 2015-06-21      Webb  1.0000000
## 2363 2015-06-22      Webb  1.0000000
## 2364 2015-06-23      Webb  2.0000000
## 2365 2015-06-24      Webb        NaN
## 2366 2015-06-25      Webb        NaN
## 2367 2015-06-26      Webb  1.0000000
## 2368 2015-06-27      Webb  1.0000000
## 2369 2015-06-28      Webb  1.0000000
## 2370 2015-06-29      Webb  1.0000000
## 2371 2015-06-30      Webb        NaN
## 2372 2015-07-01      Webb        NaN
## 2373 2015-07-02      Webb        NaN
## 2374 2015-07-03      Webb        NaN
## 2375 2015-07-04      Webb  1.0000000
## 2376 2015-07-05      Webb  1.0000000
## 2377 2015-07-06      Webb  1.0000000
## 2378 2015-07-07      Webb        NaN
## 2379 2015-07-08      Webb        NaN
## 2380 2015-07-09      Webb  1.6666667
## 2381 2015-07-10      Webb  1.6666667
## 2382 2015-07-11      Webb  1.6666667
## 2383 2015-07-12      Webb  1.6666667
## 2384 2015-07-13      Webb  1.5000000
## 2385 2015-07-14      Webb  1.5000000
## 2386 2015-07-15      Webb  1.0000000
## 2387 2015-07-16      Webb  2.0000000
## 2388 2015-07-17      Webb  1.0000000
## 2389 2015-07-18      Webb  0.6666667
## 2390 2015-07-19      Webb  0.6666667
## 2391 2015-07-20      Webb  1.6666667
## 2392 2015-07-21      Webb  5.0000000
## 2393 2015-07-22      Webb  1.0000000
## 2394 2015-07-23      Webb  1.0000000
## 2395 2015-07-24      Webb  1.0000000
## 2396 2015-07-25      Webb  1.0000000
## 2397 2015-07-26      Webb  1.5000000
## 2398 2015-07-27      Webb  1.5000000
## 2399 2015-07-28      Webb  1.5000000
## 2400 2015-07-29      Webb  3.0000000
## 2401 2015-07-30      Webb  2.0000000
## 2402 2015-07-31      Webb  1.7500000
## 2403 2015-08-01      Webb  1.4000000
## 2404 2015-08-02      Webb  1.4000000
## 2405 2015-08-03      Webb  1.3333333
## 2406 2015-08-04      Webb  1.0000000
## 2407 2015-08-05      Webb  0.0000000
## 2408 2015-08-06      Webb        NaN
## 2409 2015-08-07      Webb  2.0000000
## 2410 2015-08-08      Webb  1.5000000
## 2411 2015-08-09      Webb  1.5000000
## 2412 2015-08-10      Webb  1.0000000
## 2413 2015-08-11      Webb  1.0000000
## 2414 2015-08-12      Webb  1.0000000
## 2415 2015-08-13      Webb  1.0000000
## 2416 2015-08-14      Webb  1.0000000
## 2417 2015-08-15      Webb  1.0000000
## 2418 2015-08-16      Webb  1.0000000
## 2419 2015-08-17      Webb  1.0000000
## 2420 2015-08-18      Webb  1.0000000
## 2421 2015-08-19      Webb  1.0000000
## 2422 2015-08-20      Webb  1.0000000
## 2423 2015-08-21      Webb  1.5000000
## 2424 2015-08-22      Webb  1.6666667
## 2425 2015-08-23      Webb  1.5000000
## 2426 2015-08-24      Webb  1.5000000
## 2427 2015-08-25      Webb  1.5000000
## 2428 2015-08-26      Webb  2.0000000
## 2429 2015-08-27      Webb        NaN
## 2430 2015-08-28      Webb  2.0000000
## 2431 2015-08-29      Webb  1.5000000
## 2432 2015-08-30      Webb  1.5000000
## 2433 2015-08-31      Webb  0.6666667
## 2434 2015-09-01      Webb  0.6666667
## 2435 2015-09-02      Webb  0.5000000
## 2436 2015-09-03      Webb        NaN
## 2437 2015-09-04      Webb  1.5000000
## 2438 2015-09-05      Webb  1.0000000
## 2439 2015-09-06      Webb  1.0000000
## 2440 2015-09-07      Webb  1.0000000
## 2441 2015-09-08      Webb  1.0000000
## 2442 2015-09-09      Webb  0.5000000
## 2443 2015-09-10      Webb  1.0000000
## 2444 2015-09-11      Webb  1.3333333
## 2445 2015-09-12      Webb  1.2500000
## 2446 2015-09-13      Webb  1.2500000
## 2447 2015-09-14      Webb  1.0000000
## 2448 2015-09-15      Webb  1.0000000
## 2449 2015-09-16      Webb  1.0000000
## 2450 2015-09-17      Webb  0.5000000
## 2451 2015-09-18      Webb  1.0000000
## 2452 2015-09-19      Webb  0.8571429
## 2453 2015-09-20      Webb  0.8571429
## 2454 2015-09-21      Webb  0.8333333
## 2455 2015-09-22      Webb  0.7500000
## 2456 2015-09-23      Webb  0.6666667
## 2457 2015-09-24      Webb  0.7500000
## 2458 2015-09-25      Webb  0.7500000
## 2459 2015-09-26      Webb  0.6000000
## 2460 2015-09-27      Webb  0.6000000
## 2461 2015-09-28      Webb  0.5000000
## 2462 2015-09-29      Webb  0.6666667
## 2463 2015-09-30      Webb  1.3333333
## 2464 2015-10-01      Webb  1.7500000
## 2465 2015-10-02      Webb  1.2500000
## 2466 2015-10-03      Webb  1.2500000
## 2467 2015-10-04      Webb  1.2500000
## 2468 2015-10-05      Webb  1.0000000
## 2469 2015-10-06      Webb        NaN
## 2470 2015-10-07      Webb        NaN
## 2471 2015-10-08      Webb  0.5000000
## 2472 2015-10-09      Webb  0.5000000
## 2473 2015-10-10      Webb  0.3333333
## 2474 2015-10-11      Webb  0.3333333
## 2475 2015-10-12      Webb  0.3333333
## 2476 2015-10-13      Webb  1.0000000
## 2477 2015-10-14      Webb  1.0000000
## 2478 2015-10-15      Webb  1.1666667
## 2479 2015-10-16      Webb  1.1666667
## 2480 2015-10-17      Webb  1.1666667
## 2481 2015-10-18      Webb  1.2500000
## 2482 2015-10-19      Webb  1.0000000
## 2483 2015-10-20      Webb        NaN
## 2484 2015-10-21      Webb        NaN
## 2485 2015-10-22      Webb        NaN
## 2486 2015-10-23      Webb        NaN
## 2487 2015-10-24      Webb        NaN
## 2488 2015-10-25      Webb        NaN
## 2489 2015-10-26      Webb        NaN
## 2490 2015-10-27      Webb        NaN
## 2491 2015-10-28      Webb        NaN
## 2492 2015-10-29      Webb        NaN
## 2493 2015-10-30      Webb        NaN
## 2494 2015-10-31      Webb        NaN
## 2495 2015-11-01      Webb        NaN
## 2496 2015-11-02      Webb        NaN
## 2497 2015-11-03      Webb        NaN
## 2498 2015-11-04      Webb        NaN
## 2499 2015-11-05      Webb        NaN
## 2500 2015-11-06      Webb        NaN
## 2501 2015-11-07      Webb        NaN
## 2502 2015-11-08      Webb        NaN
## 2503 2015-11-09      Webb        NaN
## 2504 2015-11-10      Webb        NaN
## 2505 2015-11-11      Webb        NaN
## 2506 2015-11-12      Webb        NaN
## 2507 2015-11-13      Webb        NaN
## 2508 2015-11-14      Webb        NaN
## 2509 2015-11-15      Webb        NaN
## 2510 2015-11-16      Webb        NaN
## 2511 2015-11-17      Webb        NaN
## 2512 2015-11-18      Webb        NaN
## 2513 2015-11-19      Webb        NaN
## 2514 2015-11-20      Webb        NaN
## 2515 2015-11-21      Webb        NaN
## 2516 2015-11-22      Webb        NaN
## 2517 2015-11-23      Webb        NaN
## 2518 2015-11-24      Webb        NaN
## 2519 2015-11-25      Webb        NaN
## 2520 2015-11-26      Webb        NaN
## 2521 2015-11-27      Webb        NaN
## 2522 2015-11-28      Webb        NaN
## 2523 2015-11-29      Webb        NaN
## 2524 2015-11-30      Webb        NaN
## 2525 2015-12-01      Webb        NaN
## 2526 2015-12-02      Webb        NaN
## 2527 2015-12-03      Webb        NaN
## 2528 2015-12-04      Webb        NaN
## 2529 2015-12-05      Webb        NaN
## 2530 2015-12-06      Webb        NaN
## 2531 2015-12-07      Webb        NaN
## 2532 2015-12-08      Webb        NaN
## 2533 2015-12-09      Webb        NaN
## 2534 2015-12-10      Webb        NaN
## 2535 2015-12-11      Webb        NaN
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## 2537 2015-12-13      Webb        NaN
## 2538 2015-12-14      Webb        NaN
## 2539 2015-12-15      Webb        NaN
## 2540 2015-12-16      Webb        NaN
## 2541 2015-12-17      Webb        NaN
## 2542 2015-12-18      Webb        NaN
## 2543 2015-12-19      Webb        NaN
## 2544 2015-12-20      Webb        NaN
## 2545 2015-12-21      Webb        NaN
## 2546 2015-12-22      Webb        NaN
## 2547 2015-12-23      Webb        NaN
## 2548 2015-12-24      Webb        NaN
## 2549 2015-12-25      Webb        NaN
## 2550 2015-12-26      Webb        NaN
## 2551 2015-12-27      Webb        NaN
## 2552 2015-12-28      Webb        NaN
## 2553 2015-12-29      Webb        NaN
## 2554 2015-12-30      Webb        NaN
## 2555 2015-12-31      Webb        NaN
## 2556 2015-01-01 Undecided        NaN
## 2557 2015-01-02 Undecided        NaN
## 2558 2015-01-03 Undecided        NaN
## 2559 2015-01-04 Undecided        NaN
## 2560 2015-01-05 Undecided        NaN
## 2561 2015-01-06 Undecided        NaN
## 2562 2015-01-07 Undecided        NaN
## 2563 2015-01-08 Undecided        NaN
## 2564 2015-01-09 Undecided        NaN
## 2565 2015-01-10 Undecided  9.0000000
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## 2571 2015-01-16 Undecided        NaN
## 2572 2015-01-17 Undecided        NaN
## 2573 2015-01-18 Undecided        NaN
## 2574 2015-01-19 Undecided        NaN
## 2575 2015-01-20 Undecided        NaN
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## 2577 2015-01-22 Undecided 11.0000000
## 2578 2015-01-23 Undecided 11.0000000
## 2579 2015-01-24 Undecided 11.0000000
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## 2587 2015-02-01 Undecided        NaN
## 2588 2015-02-02 Undecided        NaN
## 2589 2015-02-03 Undecided        NaN
## 2590 2015-02-04 Undecided        NaN
## 2591 2015-02-05 Undecided        NaN
## 2592 2015-02-06 Undecided        NaN
## 2593 2015-02-07 Undecided        NaN
## 2594 2015-02-08 Undecided        NaN
## 2595 2015-02-09 Undecided        NaN
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## 2599 2015-02-13 Undecided  3.0000000
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## 2621 2015-03-07 Undecided  9.0000000
## 2622 2015-03-08 Undecided  9.0000000
## 2623 2015-03-09 Undecided  9.0000000
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## 2656 2015-04-11 Undecided  8.0000000
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## 2658 2015-04-13 Undecided  8.0000000
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## 2663 2015-04-18 Undecided 10.3333333
## 2664 2015-04-19 Undecided  9.0000000
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## 2666 2015-04-21 Undecided 11.0000000
## 2667 2015-04-22 Undecided        NaN
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## 2692 2015-05-17 Undecided 11.0000000
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## 2707 2015-06-01 Undecided  8.5000000
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## 2749 2015-07-13 Undecided 11.0000000
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## 2810 2015-09-12 Undecided  7.0000000
## 2811 2015-09-13 Undecided  7.0000000
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## 2819 2015-09-21 Undecided  6.5000000
## 2820 2015-09-22 Undecided  5.0000000
## 2821 2015-09-23 Undecided  5.6666667
## 2822 2015-09-24 Undecided  7.5000000
## 2823 2015-09-25 Undecided  7.5000000
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## 2826 2015-09-28 Undecided  6.7500000
## 2827 2015-09-29 Undecided  4.3333333
## 2828 2015-09-30 Undecided  4.0000000
## 2829 2015-10-01 Undecided  7.0000000
## 2830 2015-10-02 Undecided  8.5000000
## 2831 2015-10-03 Undecided  8.4000000
## 2832 2015-10-04 Undecided  8.4000000
## 2833 2015-10-05 Undecided 10.3333333
## 2834 2015-10-06 Undecided  8.0000000
## 2835 2015-10-07 Undecided  8.0000000
## 2836 2015-10-08 Undecided 10.5000000
## 2837 2015-10-09 Undecided 10.5000000
## 2838 2015-10-10 Undecided  8.0000000
## 2839 2015-10-11 Undecided  8.0000000
## 2840 2015-10-12 Undecided  8.0000000
## 2841 2015-10-13 Undecided  7.5000000
## 2842 2015-10-14 Undecided  6.6666667
## 2843 2015-10-15 Undecided  7.8333333
## 2844 2015-10-16 Undecided  7.3333333
## 2845 2015-10-17 Undecided  7.5714286
## 2846 2015-10-18 Undecided  8.4000000
## 2847 2015-10-19 Undecided 10.5000000
## 2848 2015-10-20 Undecided  9.0000000
## 2849 2015-10-21 Undecided  9.0000000
## 2850 2015-10-22 Undecided 10.0000000
## 2851 2015-10-23 Undecided  8.0000000
## 2852 2015-10-24 Undecided  9.3333333
## 2853 2015-10-25 Undecided  7.7500000
## 2854 2015-10-26 Undecided 11.2500000
## 2855 2015-10-27 Undecided  8.2500000
## 2856 2015-10-28 Undecided  9.0000000
## 2857 2015-10-29 Undecided  9.8333333
## 2858 2015-10-30 Undecided 11.0000000
## 2859 2015-10-31 Undecided 10.6000000
## 2860 2015-11-01 Undecided  9.5000000
## 2861 2015-11-02 Undecided  9.4000000
## 2862 2015-11-03 Undecided  9.0000000
## 2863 2015-11-04 Undecided  6.5000000
## 2864 2015-11-05 Undecided  7.0000000
## 2865 2015-11-06 Undecided  6.3333333
## 2866 2015-11-07 Undecided  7.2500000
## 2867 2015-11-08 Undecided  7.2500000
## 2868 2015-11-09 Undecided  7.0000000
## 2869 2015-11-10 Undecided  7.5000000
## 2870 2015-11-11 Undecided 10.0000000
## 2871 2015-11-12 Undecided        NaN
## 2872 2015-11-13 Undecided  9.0000000
## 2873 2015-11-14 Undecided 11.0000000
## 2874 2015-11-15 Undecided 10.2500000
## 2875 2015-11-16 Undecided  8.0000000
## 2876 2015-11-17 Undecided  7.8333333
## 2877 2015-11-18 Undecided  6.6666667
## 2878 2015-11-19 Undecided  4.0000000
## 2879 2015-11-20 Undecided  5.0000000
## 2880 2015-11-21 Undecided  6.5000000
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## 2882 2015-11-23 Undecided  6.6666667
## 2883 2015-11-24 Undecided  7.5000000
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## 2885 2015-11-26 Undecided  7.0000000
## 2886 2015-11-27 Undecided  5.0000000
## 2887 2015-11-28 Undecided  6.6666667
## 2888 2015-11-29 Undecided  6.6666667
## 2889 2015-11-30 Undecided  7.0000000
## 2890 2015-12-01 Undecided  7.0000000
## 2891 2015-12-02 Undecided 10.5000000
## 2892 2015-12-03 Undecided 11.5000000
## 2893 2015-12-04 Undecided 10.2000000
## 2894 2015-12-05 Undecided 10.4000000
## 2895 2015-12-06 Undecided  9.1666667
## 2896 2015-12-07 Undecided  7.7500000
## 2897 2015-12-08 Undecided  5.6666667
## 2898 2015-12-09 Undecided  5.6666667
## 2899 2015-12-10 Undecided  9.5000000
## 2900 2015-12-11 Undecided 10.3333333
## 2901 2015-12-12 Undecided 10.2500000
## 2902 2015-12-13 Undecided 10.2500000
## 2903 2015-12-14 Undecided 11.0000000
## 2904 2015-12-15 Undecided 11.0000000
## 2905 2015-12-16 Undecided  7.0000000
## 2906 2015-12-17 Undecided  5.5000000
## 2907 2015-12-18 Undecided        NaN
## 2908 2015-12-19 Undecided        NaN
## 2909 2015-12-20 Undecided        NaN
## 2910 2015-12-21 Undecided        NaN
## 2911 2015-12-22 Undecided        NaN
## 2912 2015-12-23 Undecided        NaN
## 2913 2015-12-24 Undecided        NaN
## 2914 2015-12-25 Undecided        NaN
## 2915 2015-12-26 Undecided        NaN
## 2916 2015-12-27 Undecided        NaN
## 2917 2015-12-28 Undecided        NaN
## 2918 2015-12-29 Undecided        NaN
## 2919 2015-12-30 Undecided        NaN
## 2920 2015-12-31 Undecided        NaN
demgtrends <- pres_trendDF.melt[pres_trendDF.melt$candidate=='Hillary' | pres_trendDF.melt$candidate=='Bernie',]
dempolls <- averagedDEMPoll.melt[averagedDEMPoll.melt$candidate=='Clinton' | averagedDEMPoll.melt$candidate=='Sanders',]
dembubbleDF <- data.frame(Date=demgtrends$date, Candidate=demgtrends$candidate, Searched=demgtrends$searched, Percentage=dempolls$percentage)
#adding a polling percentage column

dembubbleDF$Percentage[1] <- 0
#Fixing the Percentage data so that there are no more NA's

for(i in seq_along(dembubbleDF$Percentage[1:length(dembubbleDF$Percentage)])){
    if(i > 1){
        if(is.nan(dembubbleDF$Percentage[i])){
            if(!is.nan(dembubbleDF$Percentage[i-1])){
                    z <- dembubbleDF$Percentage[i-1]
            }else{
                for(n in dembubbleDF$Percentage[i:length(dembubbleDF$Percentage)]){
                    if(!is.na(n)) {
                        z <- n
                        break
                    }
                }
            }
            dembubbleDF$Percentage[i] <- z
        }
    }
}

changeInPercentage <- c(0)
for(i in seq_along(dembubbleDF$Percentage[1:length(dembubbleDF$Percentage)])){
    if(i > 1){
        z <- dembubbleDF$Percentage[i] - dembubbleDF$Percentage[i-1]
        changeInPercentage <- append(changeInPercentage, z)}
}

dembubbleDF <- cbind(dembubbleDF, Change.In.Polling.Percentage=changeInPercentage)

save(dembubbleDF, file="dembubbledf.RData")

Creating the Bubble Chart Graph

Now it was time to use the Bubble Data Frames to create the Bubble Charts. I proceeded to look at tutorials that detailed way to achieve similar results with Hans Rosling. One method was to look at the tool he used, which was Gapminder. However, after further research, I found out that Gapminder was bought by Google, which made it into an R packaged called googlevis. However, this used Adobe Flash technology to disply the results on a webpage, and since I wanted to do this project in R (as well as having a programmers distaste for Flash), I decided to go against this.

The second option was to produce the motion picture using the animation package. However, after reading the documentation, I could not really figure this package out. Therefore, what I ended up doing is to create 2 sets of 365 PNG images, and combining them in a non-linear video editor to create a single video for each party.

(as a note, the code can only be run through a special environment I have set up. If you want to recreate this process, there are notes in the code)

Creating the PNG’s for the Democratic Party

source code is at: http://andrewshinsuke.me/cs125/saveDEMVideo.r

##CODE WILL NOT WORK AS IS to PRODUCE THE 365 PNG'S ALTHOUGH IT WILL RUN WITHOUT A PROBLEM
##TO RUN THIS AS I RAN IT, YOU WILL NEED A IDE WITH A MACROS RECORDING SYSTEM
##FOR SUBLIME TEXT 3, THIS IS CTRL-Q TO RECORD, CTRL-Q TO STOP RECORDING, CTRL-SHIFT-Q TO ACTIVATE THE KEYSTROKES.

##THEREFORE IT IS A GIVEN THAT YOU WILL NEED SUBLIME TEXT, A PACKAGED CALLED REPL WHICH INSTALLS R AND PYTHON WORKING ENVIRONMENTS WITHIN SUBLIME, CUSTOM KEYBINDINGS FOR REPL, WHICH FOR ME, I HAVE IT SET TO COMMAND-OPT-S TO SELECTIVELY RUN SOMETHING

##STARTING RECORDING AT THE BEGINNING OF day1 <-..., OPT-SHIFT-RIGHT, COMMAND-RIGHT, COMMAND-SHIFT-LEFT, DELETE, COMMAND-UP, OPT-DOWN, DOWN, OPT-RIGHT, COMMAND-V, TYPE 'plot', OPT-RIGHT-RIGHT-RIGHT, OPT-SHIFT-LEFT, COMMAND-V, OPT-DOWN, DOWN, COMMAND-RIGHT, OPT-LEFT-LEFT-LEFT-LEFT-LEFT-LEFT, OPT-SHIFT-RIGHT, COMMANDV, OPT-DOWN, DOWN, COMMAND-SHIFT-UP, OPT-COMMAND-S, DOWN.

load(url("http://andrewshinsuke.me/cs125/dembubbledf.RData"))

#The bottom step of creating individual png's were done manually with Sublime Macro's
#In essence, I repeated replacing the day1:day365 information using an automated process that recorded my key strokes
#After getting 365 PNG Files, I put those files into a video editor and merged them into a single video


day1 <- dembubbleDF[c(1, 366),]
day2 <- dembubbleDF[c(2, 367),]
day3 <- dembubbleDF[c(3, 368),]
day4 <- dembubbleDF[c(4, 369),]
day5 <- dembubbleDF[c(5, 370),]
day6 <- dembubbleDF[c(6, 371),]
day7 <- dembubbleDF[c(7, 372),]
day8 <- dembubbleDF[c(8, 373),]
day9 <- dembubbleDF[c(9, 374),]
day10 <- dembubbleDF[c(10, 375),]
day11 <- dembubbleDF[c(11, 376),]
day12 <- dembubbleDF[c(12, 377),]
day13 <- dembubbleDF[c(13, 378),]
day14 <- dembubbleDF[c(14, 379),]
day15 <- dembubbleDF[c(15, 380),]
day16 <- dembubbleDF[c(16, 381),]
day17 <- dembubbleDF[c(17, 382),]
day18 <- dembubbleDF[c(18, 383),]
day19 <- dembubbleDF[c(19, 384),]
day20 <- dembubbleDF[c(20, 385),]
day21 <- dembubbleDF[c(21, 386),]
day22 <- dembubbleDF[c(22, 387),]
day23 <- dembubbleDF[c(23, 388),]
day24 <- dembubbleDF[c(24, 389),]
day25 <- dembubbleDF[c(25, 390),]
day26 <- dembubbleDF[c(26, 391),]
day27 <- dembubbleDF[c(27, 392),]
day28 <- dembubbleDF[c(28, 393),]
day29 <- dembubbleDF[c(29, 394),]
day30 <- dembubbleDF[c(30, 395),]
day31 <- dembubbleDF[c(31, 396),]
day32 <- dembubbleDF[c(32, 397),]
day33 <- dembubbleDF[c(33, 398),]
day34 <- dembubbleDF[c(34, 399),]
day35 <- dembubbleDF[c(35, 400),]
day36 <- dembubbleDF[c(36, 401),]
day37 <- dembubbleDF[c(37, 402),]
day38 <- dembubbleDF[c(38, 403),]
day39 <- dembubbleDF[c(39, 404),]
day40 <- dembubbleDF[c(40, 405),]
day41 <- dembubbleDF[c(41, 406),]
day42 <- dembubbleDF[c(42, 407),]
day43 <- dembubbleDF[c(43, 408),]
day44 <- dembubbleDF[c(44, 409),]
day45 <- dembubbleDF[c(45, 410),]
day46 <- dembubbleDF[c(46, 411),]
day47 <- dembubbleDF[c(47, 412),]
day48 <- dembubbleDF[c(48, 413),]
day49 <- dembubbleDF[c(49, 414),]
day50 <- dembubbleDF[c(50, 415),]
day51 <- dembubbleDF[c(51, 416),]
day52 <- dembubbleDF[c(52, 417),]
day53 <- dembubbleDF[c(53, 418),]
day54 <- dembubbleDF[c(54, 419),]
day55 <- dembubbleDF[c(55, 420),]
day56 <- dembubbleDF[c(56, 421),]
day57 <- dembubbleDF[c(57, 422),]
day58 <- dembubbleDF[c(58, 423),]
day59 <- dembubbleDF[c(59, 424),]
day60 <- dembubbleDF[c(60, 425),]
day61 <- dembubbleDF[c(61, 426),]
day62 <- dembubbleDF[c(62, 427),]
day63 <- dembubbleDF[c(63, 428),]
day64 <- dembubbleDF[c(64, 429),]
day65 <- dembubbleDF[c(65, 430),]
day66 <- dembubbleDF[c(66, 431),]
day67 <- dembubbleDF[c(67, 432),]
day68 <- dembubbleDF[c(68, 433),]
day69 <- dembubbleDF[c(69, 434),]
day70 <- dembubbleDF[c(70, 435),]
day71 <- dembubbleDF[c(71, 436),]
day72 <- dembubbleDF[c(72, 437),]
day73 <- dembubbleDF[c(73, 438),]
day74 <- dembubbleDF[c(74, 439),]
day75 <- dembubbleDF[c(75, 440),]
day76 <- dembubbleDF[c(76, 441),]
day77 <- dembubbleDF[c(77, 442),]
day78 <- dembubbleDF[c(78, 443),]
day79 <- dembubbleDF[c(79, 444),]
day80 <- dembubbleDF[c(80, 445),]
day81 <- dembubbleDF[c(81, 446),]
day82 <- dembubbleDF[c(82, 447),]
day83 <- dembubbleDF[c(83, 448),]
day84 <- dembubbleDF[c(84, 449),]
day85 <- dembubbleDF[c(85, 450),]
day86 <- dembubbleDF[c(86, 451),]
day87 <- dembubbleDF[c(87, 452),]
day88 <- dembubbleDF[c(88, 453),]
day89 <- dembubbleDF[c(89, 454),]
day90 <- dembubbleDF[c(90, 455),]
day91 <- dembubbleDF[c(91, 456),]
day92 <- dembubbleDF[c(92, 457),]
day93 <- dembubbleDF[c(93, 458),]
day94 <- dembubbleDF[c(94, 459),]
day95 <- dembubbleDF[c(95, 460),]
day96 <- dembubbleDF[c(96, 461),]
day97 <- dembubbleDF[c(97, 462),]
day98 <- dembubbleDF[c(98, 463),]
day99 <- dembubbleDF[c(99, 464),]
day100 <- dembubbleDF[c(100, 465),]
day101 <- dembubbleDF[c(101, 466),]
day102 <- dembubbleDF[c(102, 467),]
day103 <- dembubbleDF[c(103, 468),]
day104 <- dembubbleDF[c(104, 469),]
day105 <- dembubbleDF[c(105, 470),]
day106 <- dembubbleDF[c(106, 471),]
day107 <- dembubbleDF[c(107, 472),]
day108 <- dembubbleDF[c(108, 473),]
day109 <- dembubbleDF[c(109, 474),]
day110 <- dembubbleDF[c(110, 475),]
day111 <- dembubbleDF[c(111, 476),]
day112 <- dembubbleDF[c(112, 477),]
day113 <- dembubbleDF[c(113, 478),]
day114 <- dembubbleDF[c(114, 479),]
day115 <- dembubbleDF[c(115, 480),]
day116 <- dembubbleDF[c(116, 481),]
day117 <- dembubbleDF[c(117, 482),]
day118 <- dembubbleDF[c(118, 483),]
day119 <- dembubbleDF[c(119, 484),]
day120 <- dembubbleDF[c(120, 485),]
day121 <- dembubbleDF[c(121, 486),]
day122 <- dembubbleDF[c(122, 487),]
day123 <- dembubbleDF[c(123, 488),]
day124 <- dembubbleDF[c(124, 489),]
day125 <- dembubbleDF[c(125, 490),]
day126 <- dembubbleDF[c(126, 491),]
day127 <- dembubbleDF[c(127, 492),]
day128 <- dembubbleDF[c(128, 493),]
day129 <- dembubbleDF[c(129, 494),]
day130 <- dembubbleDF[c(130, 495),]
day131 <- dembubbleDF[c(131, 496),]
day132 <- dembubbleDF[c(132, 497),]
day133 <- dembubbleDF[c(133, 498),]
day134 <- dembubbleDF[c(134, 499),]
day135 <- dembubbleDF[c(135, 500),]
day136 <- dembubbleDF[c(136, 501),]
day137 <- dembubbleDF[c(137, 502),]
day138 <- dembubbleDF[c(138, 503),]
day139 <- dembubbleDF[c(139, 504),]
day140 <- dembubbleDF[c(140, 505),]
day141 <- dembubbleDF[c(141, 506),]
day142 <- dembubbleDF[c(142, 507),]
day143 <- dembubbleDF[c(143, 508),]
day144 <- dembubbleDF[c(144, 509),]
day145 <- dembubbleDF[c(145, 510),]
day146 <- dembubbleDF[c(146, 511),]
day147 <- dembubbleDF[c(147, 512),]
day148 <- dembubbleDF[c(148, 513),]
day149 <- dembubbleDF[c(149, 514),]
day150 <- dembubbleDF[c(150, 515),]
day151 <- dembubbleDF[c(151, 516),]
day152 <- dembubbleDF[c(152, 517),]
day153 <- dembubbleDF[c(153, 518),]
day154 <- dembubbleDF[c(154, 519),]
day155 <- dembubbleDF[c(155, 520),]
day156 <- dembubbleDF[c(156, 521),]
day157 <- dembubbleDF[c(157, 522),]
day158 <- dembubbleDF[c(158, 523),]
day159 <- dembubbleDF[c(159, 524),]
day160 <- dembubbleDF[c(160, 525),]
day161 <- dembubbleDF[c(161, 526),]
day162 <- dembubbleDF[c(162, 527),]
day163 <- dembubbleDF[c(163, 528),]
day164 <- dembubbleDF[c(164, 529),]
day165 <- dembubbleDF[c(165, 530),]
day166 <- dembubbleDF[c(166, 531),]
day167 <- dembubbleDF[c(167, 532),]
day168 <- dembubbleDF[c(168, 533),]
day169 <- dembubbleDF[c(169, 534),]
day170 <- dembubbleDF[c(170, 535),]
day171 <- dembubbleDF[c(171, 536),]
day172 <- dembubbleDF[c(172, 537),]
day173 <- dembubbleDF[c(173, 538),]
day174 <- dembubbleDF[c(174, 539),]
day175 <- dembubbleDF[c(175, 540),]
day176 <- dembubbleDF[c(176, 541),]
day177 <- dembubbleDF[c(177, 542),]
day178 <- dembubbleDF[c(178, 543),]
day179 <- dembubbleDF[c(179, 544),]
day180 <- dembubbleDF[c(180, 545),]
day181 <- dembubbleDF[c(181, 546),]
day182 <- dembubbleDF[c(182, 547),]
day183 <- dembubbleDF[c(183, 548),]
day184 <- dembubbleDF[c(184, 549),]
day185 <- dembubbleDF[c(185, 550),]
day186 <- dembubbleDF[c(186, 551),]
day187 <- dembubbleDF[c(187, 552),]
day188 <- dembubbleDF[c(188, 553),]
day189 <- dembubbleDF[c(189, 554),]
day190 <- dembubbleDF[c(190, 555),]
day191 <- dembubbleDF[c(191, 556),]
day192 <- dembubbleDF[c(192, 557),]
day193 <- dembubbleDF[c(193, 558),]
day194 <- dembubbleDF[c(194, 559),]
day195 <- dembubbleDF[c(195, 560),]
day196 <- dembubbleDF[c(196, 561),]
day197 <- dembubbleDF[c(197, 562),]
day198 <- dembubbleDF[c(198, 563),]
day199 <- dembubbleDF[c(199, 564),]
day200 <- dembubbleDF[c(200, 565),]
day201 <- dembubbleDF[c(201, 566),]
day202 <- dembubbleDF[c(202, 567),]
day203 <- dembubbleDF[c(203, 568),]
day204 <- dembubbleDF[c(204, 569),]
day205 <- dembubbleDF[c(205, 570),]
day206 <- dembubbleDF[c(206, 571),]
day207 <- dembubbleDF[c(207, 572),]
day208 <- dembubbleDF[c(208, 573),]
day209 <- dembubbleDF[c(209, 574),]
day210 <- dembubbleDF[c(210, 575),]
day211 <- dembubbleDF[c(211, 576),]
day212 <- dembubbleDF[c(212, 577),]
day213 <- dembubbleDF[c(213, 578),]
day214 <- dembubbleDF[c(214, 579),]
day215 <- dembubbleDF[c(215, 580),]
day216 <- dembubbleDF[c(216, 581),]
day217 <- dembubbleDF[c(217, 582),]
day218 <- dembubbleDF[c(218, 583),]
day219 <- dembubbleDF[c(219, 584),]
day220 <- dembubbleDF[c(220, 585),]
day221 <- dembubbleDF[c(221, 586),]
day222 <- dembubbleDF[c(222, 587),]
day223 <- dembubbleDF[c(223, 588),]
day224 <- dembubbleDF[c(224, 589),]
day225 <- dembubbleDF[c(225, 590),]
day226 <- dembubbleDF[c(226, 591),]
day227 <- dembubbleDF[c(227, 592),]
day228 <- dembubbleDF[c(228, 593),]
day229 <- dembubbleDF[c(229, 594),]
day230 <- dembubbleDF[c(230, 595),]
day231 <- dembubbleDF[c(231, 596),]
day232 <- dembubbleDF[c(232, 597),]
day233 <- dembubbleDF[c(233, 598),]
day234 <- dembubbleDF[c(234, 599),]
day235 <- dembubbleDF[c(235, 600),]
day236 <- dembubbleDF[c(236, 601),]
day237 <- dembubbleDF[c(237, 602),]
day238 <- dembubbleDF[c(238, 603),]
day239 <- dembubbleDF[c(239, 604),]
day240 <- dembubbleDF[c(240, 605),]
day241 <- dembubbleDF[c(241, 606),]
day242 <- dembubbleDF[c(242, 607),]
day243 <- dembubbleDF[c(243, 608),]
day244 <- dembubbleDF[c(244, 609),]
day245 <- dembubbleDF[c(245, 610),]
day246 <- dembubbleDF[c(246, 611),]
day247 <- dembubbleDF[c(247, 612),]
day248 <- dembubbleDF[c(248, 613),]
day249 <- dembubbleDF[c(249, 614),]
day250 <- dembubbleDF[c(250, 615),]
day251 <- dembubbleDF[c(251, 616),]
day252 <- dembubbleDF[c(252, 617),]
day253 <- dembubbleDF[c(253, 618),]
day254 <- dembubbleDF[c(254, 619),]
day255 <- dembubbleDF[c(255, 620),]
day256 <- dembubbleDF[c(256, 621),]
day257 <- dembubbleDF[c(257, 622),]
day258 <- dembubbleDF[c(258, 623),]
day259 <- dembubbleDF[c(259, 624),]
day260 <- dembubbleDF[c(260, 625),]
day261 <- dembubbleDF[c(261, 626),]
day262 <- dembubbleDF[c(262, 627),]
day263 <- dembubbleDF[c(263, 628),]
day264 <- dembubbleDF[c(264, 629),]
day265 <- dembubbleDF[c(265, 630),]
day266 <- dembubbleDF[c(266, 631),]
day267 <- dembubbleDF[c(267, 632),]
day268 <- dembubbleDF[c(268, 633),]
day269 <- dembubbleDF[c(269, 634),]
day270 <- dembubbleDF[c(270, 635),]
day271 <- dembubbleDF[c(271, 636),]
day272 <- dembubbleDF[c(272, 637),]
day273 <- dembubbleDF[c(273, 638),]
day274 <- dembubbleDF[c(274, 639),]
day275 <- dembubbleDF[c(275, 640),]
day276 <- dembubbleDF[c(276, 641),]
day277 <- dembubbleDF[c(277, 642),]
day278 <- dembubbleDF[c(278, 643),]
day279 <- dembubbleDF[c(279, 644),]
day280 <- dembubbleDF[c(280, 645),]
day281 <- dembubbleDF[c(281, 646),]
day282 <- dembubbleDF[c(282, 647),]
day283 <- dembubbleDF[c(283, 648),]
day284 <- dembubbleDF[c(284, 649),]
day285 <- dembubbleDF[c(285, 650),]
day286 <- dembubbleDF[c(286, 651),]
day287 <- dembubbleDF[c(287, 652),]
day288 <- dembubbleDF[c(288, 653),]
day289 <- dembubbleDF[c(289, 654),]
day290 <- dembubbleDF[c(290, 655),]
day291 <- dembubbleDF[c(291, 656),]
day292 <- dembubbleDF[c(292, 657),]
day293 <- dembubbleDF[c(293, 658),]
day294 <- dembubbleDF[c(294, 659),]
day295 <- dembubbleDF[c(295, 660),]
day296 <- dembubbleDF[c(296, 661),]
day297 <- dembubbleDF[c(297, 662),]
day298 <- dembubbleDF[c(298, 663),]
day299 <- dembubbleDF[c(299, 664),]
day300 <- dembubbleDF[c(300, 665),]
day301 <- dembubbleDF[c(301, 666),]
day302 <- dembubbleDF[c(302, 667),]
day303 <- dembubbleDF[c(303, 668),]
day304 <- dembubbleDF[c(304, 669),]
day305 <- dembubbleDF[c(305, 670),]
day306 <- dembubbleDF[c(306, 671),]
day307 <- dembubbleDF[c(307, 672),]
day308 <- dembubbleDF[c(308, 673),]
day309 <- dembubbleDF[c(309, 674),]
day310 <- dembubbleDF[c(310, 675),]
day311 <- dembubbleDF[c(311, 676),]
day312 <- dembubbleDF[c(312, 677),]
day313 <- dembubbleDF[c(313, 678),]
day314 <- dembubbleDF[c(314, 679),]
day315 <- dembubbleDF[c(315, 680),]
day316 <- dembubbleDF[c(316, 681),]
day317 <- dembubbleDF[c(317, 682),]
day318 <- dembubbleDF[c(318, 683),]
day319 <- dembubbleDF[c(319, 684),]
day320 <- dembubbleDF[c(320, 685),]
day321 <- dembubbleDF[c(321, 686),]
day322 <- dembubbleDF[c(322, 687),]
day323 <- dembubbleDF[c(323, 688),]
day324 <- dembubbleDF[c(324, 689),]
day325 <- dembubbleDF[c(325, 690),]
day326 <- dembubbleDF[c(326, 691),]
day327 <- dembubbleDF[c(327, 692),]
day328 <- dembubbleDF[c(328, 693),]
day329 <- dembubbleDF[c(329, 694),]
day330 <- dembubbleDF[c(330, 695),]
day331 <- dembubbleDF[c(331, 696),]
day332 <- dembubbleDF[c(332, 697),]
day333 <- dembubbleDF[c(333, 698),]
day334 <- dembubbleDF[c(334, 699),]
day335 <- dembubbleDF[c(335, 700),]
day336 <- dembubbleDF[c(336, 701),]
day337 <- dembubbleDF[c(337, 702),]
day338 <- dembubbleDF[c(338, 703),]
day339 <- dembubbleDF[c(339, 704),]
day340 <- dembubbleDF[c(340, 705),]
day341 <- dembubbleDF[c(341, 706),]
day342 <- dembubbleDF[c(342, 707),]
day343 <- dembubbleDF[c(343, 708),]
day344 <- dembubbleDF[c(344, 709),]
day345 <- dembubbleDF[c(345, 710),]
day346 <- dembubbleDF[c(346, 711),]
day347 <- dembubbleDF[c(347, 712),]
day348 <- dembubbleDF[c(348, 713),]
day349 <- dembubbleDF[c(349, 714),]
day350 <- dembubbleDF[c(350, 715),]
day351 <- dembubbleDF[c(351, 716),]
day352 <- dembubbleDF[c(352, 717),]
day353 <- dembubbleDF[c(353, 718),]
day354 <- dembubbleDF[c(354, 719),]
day355 <- dembubbleDF[c(355, 720),]
day356 <- dembubbleDF[c(356, 721),]
day357 <- dembubbleDF[c(357, 722),]
day358 <- dembubbleDF[c(358, 723),]
day359 <- dembubbleDF[c(359, 724),]
day360 <- dembubbleDF[c(360, 725),]
day361 <- dembubbleDF[c(361, 726),]
day362 <- dembubbleDF[c(362, 727),]
day363 <- dembubbleDF[c(363, 728),]
day364 <- dembubbleDF[c(364, 729),]
day365 <- dembubbleDF[c(365, 730),]

plotday1 <- ggplot(day1, aes(x = Percentage, y = Change.In.Polling.Percentage, size = Searched, fill=Candidate, label=Candidate)) +
  labs(x="Total Percentage of Democrat Vote", y="Change in Percentage")+
  geom_point(shape = 21, show_guide = FALSE) +
  geom_text(size=4)+
  # map z to area and make larger circles
  scale_size_area(max_size = 70) +
  scale_x_continuous(breaks = c(20, 40, 60, 80), limit = c(0, 100), 
                     expand = c(0, 0)) +
  scale_y_continuous(breaks = c(-40, -20, 0, 20, 40), limit = c(-60, 60), 
                     expand = c(0, 0)) +
  theme_bw() +
  theme(panel.grid.major = element_blank(),
        plot.title = element_text(size = rel(1.5), face = "bold", vjust = 1.5),
        axis.title=element_blank(),
        axis.text.x=element_text(size=10),
        axis.text.y=element_text(size=10))+

ggtitle(paste(as.character(day1$Date[1]), " Democrats"))
ggsave(filename="DEMday1.png")
## Saving 7 x 5 in image

Creating the PNG’s for the GOP

The same applies here. The source code is at: http://andrewshinsuke.me/cs125/saveGOPVideo.r

require(ggplot2)
load(url("http://andrewshinsuke.me/cs125/gopbubbledf.RData"))



day1 <- gopbubbleDF[c(1, 366, 731),]
day2 <- gopbubbleDF[c(2, 367, 732),]
day3 <- gopbubbleDF[c(3, 368, 733),]
day4 <- gopbubbleDF[c(4, 369, 734),]
day5 <- gopbubbleDF[c(5, 370, 735),]
day6 <- gopbubbleDF[c(6, 371, 736),]
day7 <- gopbubbleDF[c(7, 372, 737),]
day8 <- gopbubbleDF[c(8, 373, 738),]
day9 <- gopbubbleDF[c(9, 374, 739),]
day10 <- gopbubbleDF[c(10, 375, 740),]
day11 <-gopbubbleDF[c(11, 376, 741),]
day12 <-gopbubbleDF[c(12, 377, 742),]
day13 <-gopbubbleDF[c(13, 378, 743),]
day14 <-gopbubbleDF[c(14, 379, 744),]
day15 <-gopbubbleDF[c(15, 380, 745),]
day16 <-gopbubbleDF[c(16, 381, 746),]
day17 <-gopbubbleDF[c(17, 382, 747),]
day18 <-gopbubbleDF[c(18, 383, 748),]
day19 <-gopbubbleDF[c(19, 384, 749),]
day20 <-gopbubbleDF[c(20, 385, 750),]
day21 <- gopbubbleDF[c(21, 386, 751),]
day22 <- gopbubbleDF[c(22, 387, 752),]
day23 <- gopbubbleDF[c(23, 388, 753),]
day24 <- gopbubbleDF[c(24, 389, 754),]
day25 <- gopbubbleDF[c(25, 390, 755),]
day26 <- gopbubbleDF[c(26, 391, 756),]
day27 <- gopbubbleDF[c(27, 392, 757),]
day28 <- gopbubbleDF[c(28, 393, 758),]
day29 <- gopbubbleDF[c(29, 394, 759),]
day30 <- gopbubbleDF[c(30, 395, 760),]
day31 <- gopbubbleDF[c(31, 396, 761),]
day32 <- gopbubbleDF[c(32, 397, 762),]
day33 <- gopbubbleDF[c(33, 398, 763),]
day34 <- gopbubbleDF[c(34, 399, 764),]
day35 <- gopbubbleDF[c(35, 400, 765),]
day36 <- gopbubbleDF[c(36, 401, 766),]
day37 <- gopbubbleDF[c(37, 402, 767),]
day38 <- gopbubbleDF[c(38, 403, 768),]
day39 <- gopbubbleDF[c(39, 404, 769),]
day40 <- gopbubbleDF[c(40, 405, 770),]
day41 <- gopbubbleDF[c(41, 406, 771),]
day42 <- gopbubbleDF[c(42, 407, 772),]
day43 <- gopbubbleDF[c(43, 408, 773),]
day44 <- gopbubbleDF[c(44, 409, 774),]
day45 <- gopbubbleDF[c(45, 410, 775),]
day46 <- gopbubbleDF[c(46, 411, 776),]
day47 <- gopbubbleDF[c(47, 412, 777),]
day48 <- gopbubbleDF[c(48, 413, 778),]
day49 <- gopbubbleDF[c(49, 414, 779),]
day50 <- gopbubbleDF[c(50, 415, 780),]
day51 <- gopbubbleDF[c(51, 416, 781),]
day52 <- gopbubbleDF[c(52, 417, 782),]
day53 <- gopbubbleDF[c(53, 418, 783),]
day54 <- gopbubbleDF[c(54, 419, 784),]
day55 <- gopbubbleDF[c(55, 420, 785),]
day56 <- gopbubbleDF[c(56, 421, 786),]
day57 <- gopbubbleDF[c(57, 422, 787),]
day58 <- gopbubbleDF[c(58, 423, 788),]
day59 <- gopbubbleDF[c(59, 424, 789),]
day60 <- gopbubbleDF[c(60, 425, 790),]
day61 <- gopbubbleDF[c(61, 426, 791),]
day62 <- gopbubbleDF[c(62, 427, 792),]
day63 <- gopbubbleDF[c(63, 428, 793),]
day64 <- gopbubbleDF[c(64, 429, 794),]
day65 <- gopbubbleDF[c(65, 430, 795),]
day66 <- gopbubbleDF[c(66, 431, 796),]
day67 <- gopbubbleDF[c(67, 432, 797),]
day68 <- gopbubbleDF[c(68, 433, 798),]
day69 <- gopbubbleDF[c(69, 434, 799),]
day70 <- gopbubbleDF[c(70, 435, 800),]
day71 <- gopbubbleDF[c(71, 436, 801),]
day72 <- gopbubbleDF[c(72, 437, 802),]
day73 <- gopbubbleDF[c(73, 438, 803),]
day74 <- gopbubbleDF[c(74, 439, 804),]
day75 <- gopbubbleDF[c(75, 440, 805),]
day76 <- gopbubbleDF[c(76, 441, 806),]
day77 <- gopbubbleDF[c(77, 442, 807),]
day78 <- gopbubbleDF[c(78, 443, 808),]
day79 <- gopbubbleDF[c(79, 444, 809),]
day80 <- gopbubbleDF[c(80, 445, 810),]
day81 <- gopbubbleDF[c(81, 446, 811),]
day82 <- gopbubbleDF[c(82, 447, 812),]
day83 <- gopbubbleDF[c(83, 448, 813),]
day84 <- gopbubbleDF[c(84, 449, 814),]
day85 <- gopbubbleDF[c(85, 450, 815),]
day86 <- gopbubbleDF[c(86, 451, 816),]
day87 <- gopbubbleDF[c(87, 452, 817),]
day88 <- gopbubbleDF[c(88, 453, 818),]
day89 <- gopbubbleDF[c(89, 454, 819),]
day90 <- gopbubbleDF[c(90, 455, 820),]
day91 <- gopbubbleDF[c(91, 456, 821),]
day92 <- gopbubbleDF[c(92, 457, 822),]
day93 <- gopbubbleDF[c(93, 458, 823),]
day94 <- gopbubbleDF[c(94, 459, 824),]
day95 <- gopbubbleDF[c(95, 460, 825),]
day96 <- gopbubbleDF[c(96, 461, 826),]
day97 <- gopbubbleDF[c(97, 462, 827),]
day98 <- gopbubbleDF[c(98, 463, 828),]
day99 <- gopbubbleDF[c(99, 464, 829),]
day100 <- gopbubbleDF[c(100, 465, 830),]
day101 <- gopbubbleDF[c(101, 466, 831),]
day102 <- gopbubbleDF[c(102, 467, 832),]
day103 <- gopbubbleDF[c(103, 468, 833),]
day104 <- gopbubbleDF[c(104, 469, 834),]
day105 <- gopbubbleDF[c(105, 470, 835),]
day106 <- gopbubbleDF[c(106, 471, 836),]
day107 <- gopbubbleDF[c(107, 472, 837),]
day108 <- gopbubbleDF[c(108, 473, 838),]
day109 <- gopbubbleDF[c(109, 474, 839),]
day110 <- gopbubbleDF[c(110, 475, 840),]
day111 <- gopbubbleDF[c(111, 476, 841),]
day112 <- gopbubbleDF[c(112, 477, 842),]
day113 <- gopbubbleDF[c(113, 478, 843),]
day114 <- gopbubbleDF[c(114, 479, 844),]
day115 <- gopbubbleDF[c(115, 480, 845),]
day116 <- gopbubbleDF[c(116, 481, 846),]
day117 <- gopbubbleDF[c(117, 482, 847),]
day118 <- gopbubbleDF[c(118, 483, 848),]
day119 <- gopbubbleDF[c(119, 484, 849),]
day120 <- gopbubbleDF[c(120, 485, 850),]
day121 <- gopbubbleDF[c(121, 486, 851),]
day122 <- gopbubbleDF[c(122, 487, 852),]
day123 <- gopbubbleDF[c(123, 488, 853),]
day124 <- gopbubbleDF[c(124, 489, 854),]
day125 <- gopbubbleDF[c(125, 490, 855),]
day126 <- gopbubbleDF[c(126, 491, 856),]
day127 <- gopbubbleDF[c(127, 492, 857),]
day128 <- gopbubbleDF[c(128, 493, 858),]
day129 <- gopbubbleDF[c(129, 494, 859),]
day130 <- gopbubbleDF[c(130, 495, 860),]
day131 <- gopbubbleDF[c(131, 496, 861),]
day132 <- gopbubbleDF[c(132, 497, 862),]
day133 <- gopbubbleDF[c(133, 498, 863),]
day134 <- gopbubbleDF[c(134, 499, 864),]
day135 <- gopbubbleDF[c(135, 500, 865),]
day136 <- gopbubbleDF[c(136, 501, 866),]
day137 <- gopbubbleDF[c(137, 502, 867),]
day138 <- gopbubbleDF[c(138, 503, 868),]
day139 <- gopbubbleDF[c(139, 504, 869),]
day140 <- gopbubbleDF[c(140, 505, 870),]
day141 <- gopbubbleDF[c(141, 506, 871),]
day142 <- gopbubbleDF[c(142, 507, 872),]
day143 <- gopbubbleDF[c(143, 508, 873),]
day144 <- gopbubbleDF[c(144, 509, 874),]
day145 <- gopbubbleDF[c(145, 510, 875),]
day146 <- gopbubbleDF[c(146, 511, 876),]
day147 <- gopbubbleDF[c(147, 512, 877),]
day148 <- gopbubbleDF[c(148, 513, 878),]
day149 <- gopbubbleDF[c(149, 514, 879),]
day150 <- gopbubbleDF[c(150, 515, 880),]
day151 <- gopbubbleDF[c(151, 516, 881),]
day152 <- gopbubbleDF[c(152, 517, 882),]
day153 <- gopbubbleDF[c(153, 518, 883),]
day154 <- gopbubbleDF[c(154, 519, 884),]
day155 <- gopbubbleDF[c(155, 520, 885),]
day156 <- gopbubbleDF[c(156, 521, 886),]
day157 <- gopbubbleDF[c(157, 522, 887),]
day158 <- gopbubbleDF[c(158, 523, 888),]
day159 <- gopbubbleDF[c(159, 524, 889),]
day160 <- gopbubbleDF[c(160, 525, 890),]
day161 <- gopbubbleDF[c(161, 526, 891),]
day162 <- gopbubbleDF[c(162, 527, 892),]
day163 <- gopbubbleDF[c(163, 528, 893),]
day164 <- gopbubbleDF[c(164, 529, 894),]
day165 <- gopbubbleDF[c(165, 530, 895),]
day166 <- gopbubbleDF[c(166, 531, 896),]
day167 <- gopbubbleDF[c(167, 532, 897),]
day168 <- gopbubbleDF[c(168, 533, 898),]
day169 <- gopbubbleDF[c(169, 534, 899),]
day170 <- gopbubbleDF[c(170, 535, 900),]
day171 <- gopbubbleDF[c(171, 536, 901),]
day172 <- gopbubbleDF[c(172, 537, 902),]
day173 <- gopbubbleDF[c(173, 538, 903),]
day174 <- gopbubbleDF[c(174, 539, 904),]
day175 <- gopbubbleDF[c(175, 540, 905),]
day176 <- gopbubbleDF[c(176, 541, 906),]
day177 <- gopbubbleDF[c(177, 542, 907),]
day178 <- gopbubbleDF[c(178, 543, 908),]
day179 <- gopbubbleDF[c(179, 544, 909),]
day180 <- gopbubbleDF[c(180, 545, 910),]
day181 <- gopbubbleDF[c(181, 546, 911),]
day182 <- gopbubbleDF[c(182, 547, 912),]
day183 <- gopbubbleDF[c(183, 548, 913),]
day184 <- gopbubbleDF[c(184, 549, 914),]
day185 <- gopbubbleDF[c(185, 550, 915),]
day186 <- gopbubbleDF[c(186, 551, 916),]
day187 <- gopbubbleDF[c(187, 552, 917),]
day188 <- gopbubbleDF[c(188, 553, 918),]
day189 <- gopbubbleDF[c(189, 554, 919),]
day190 <- gopbubbleDF[c(190, 555, 920),]
day191 <- gopbubbleDF[c(191, 556, 921),]
day192 <- gopbubbleDF[c(192, 557, 922),]
day193 <- gopbubbleDF[c(193, 558, 923),]
day194 <- gopbubbleDF[c(194, 559, 924),]
day195 <- gopbubbleDF[c(195, 560, 925),]
day196 <- gopbubbleDF[c(196, 561, 926),]
day197 <- gopbubbleDF[c(197, 562, 927),]
day198 <- gopbubbleDF[c(198, 563, 928),]
day199 <- gopbubbleDF[c(199, 564, 929),]
day200 <- gopbubbleDF[c(200, 565, 930),]
day201 <- gopbubbleDF[c(201, 566, 931),]
day202 <- gopbubbleDF[c(202, 567, 932),]
day203 <- gopbubbleDF[c(203, 568, 933),]
day204 <- gopbubbleDF[c(204, 569, 934),]
day205 <- gopbubbleDF[c(205, 570, 935),]
day206 <- gopbubbleDF[c(206, 571, 936),]
day207 <- gopbubbleDF[c(207, 572, 937),]
day208 <- gopbubbleDF[c(208, 573, 938),]
day209 <- gopbubbleDF[c(209, 574, 939),]
day210 <- gopbubbleDF[c(210, 575, 940),]
day211 <- gopbubbleDF[c(211, 576, 941),]
day212 <- gopbubbleDF[c(212, 577, 942),]
day213 <- gopbubbleDF[c(213, 578, 943),]
day214 <- gopbubbleDF[c(214, 579, 944),]
day215 <- gopbubbleDF[c(215, 580, 945),]
day216 <- gopbubbleDF[c(216, 581, 946),]
day217 <- gopbubbleDF[c(217, 582, 947),]
day218 <- gopbubbleDF[c(218, 583, 948),]
day219 <- gopbubbleDF[c(219, 584, 949),]
day220 <- gopbubbleDF[c(220, 585, 950),]
day221 <- gopbubbleDF[c(221, 586, 951),]
day222 <- gopbubbleDF[c(222, 587, 952),]
day223 <- gopbubbleDF[c(223, 588, 953),]
day224 <- gopbubbleDF[c(224, 589, 954),]
day225 <- gopbubbleDF[c(225, 590, 955),]
day226 <- gopbubbleDF[c(226, 591, 956),]
day227 <- gopbubbleDF[c(227, 592, 957),]
day228 <- gopbubbleDF[c(228, 593, 958),]
day229 <- gopbubbleDF[c(229, 594, 959),]
day230 <- gopbubbleDF[c(230, 595, 960),]
day231 <- gopbubbleDF[c(231, 596, 961),]
day232 <- gopbubbleDF[c(232, 597, 962),]
day233 <- gopbubbleDF[c(233, 598, 963),]
day234 <- gopbubbleDF[c(234, 599, 964),]
day235 <- gopbubbleDF[c(235, 600, 965),]
day236 <- gopbubbleDF[c(236, 601, 966),]
day237 <- gopbubbleDF[c(237, 602, 967),]
day238 <- gopbubbleDF[c(238, 603, 968),]
day239 <- gopbubbleDF[c(239, 604, 969),]
day240 <- gopbubbleDF[c(240, 605, 970),]
day241 <- gopbubbleDF[c(241, 606, 971),]
day242 <- gopbubbleDF[c(242, 607, 972),]
day243 <- gopbubbleDF[c(243, 608, 973),]
day244 <- gopbubbleDF[c(244, 609, 974),]
day245 <- gopbubbleDF[c(245, 610, 975),]
day246 <- gopbubbleDF[c(246, 611, 976),]
day247 <- gopbubbleDF[c(247, 612, 977),]
day248 <- gopbubbleDF[c(248, 613, 978),]
day249 <- gopbubbleDF[c(249, 614, 979),]
day250 <- gopbubbleDF[c(250, 615, 980),]
day251 <- gopbubbleDF[c(251, 616, 981),]
day252 <- gopbubbleDF[c(252, 617, 982),]
day253 <- gopbubbleDF[c(253, 618, 983),]
day254 <- gopbubbleDF[c(254, 619, 984),]
day255 <- gopbubbleDF[c(255, 620, 985),]
day256 <- gopbubbleDF[c(256, 621, 986),]
day257 <- gopbubbleDF[c(257, 622, 987),]
day258 <- gopbubbleDF[c(258, 623, 988),]
day259 <- gopbubbleDF[c(259, 624, 989),]
day260 <- gopbubbleDF[c(260, 625, 990),]
day261 <- gopbubbleDF[c(261, 626, 991),]
day262 <- gopbubbleDF[c(262, 627, 992),]
day263 <- gopbubbleDF[c(263, 628, 993),]
day264 <- gopbubbleDF[c(264, 629, 994),]
day265 <- gopbubbleDF[c(265, 630, 995),]
day266 <- gopbubbleDF[c(266, 631, 996),]
day267 <- gopbubbleDF[c(267, 632, 997),]
day268 <- gopbubbleDF[c(268, 633, 998),]
day269 <- gopbubbleDF[c(269, 634, 999),]
day270 <- gopbubbleDF[c(270, 635, 1000),]
day271 <- gopbubbleDF[c(271, 636, 1001),]
day272 <- gopbubbleDF[c(272, 637, 1002),]
day273 <- gopbubbleDF[c(273, 638, 1003),]
day274 <- gopbubbleDF[c(274, 639, 1004),]
day275 <- gopbubbleDF[c(275, 640, 1005),]
day276 <- gopbubbleDF[c(276, 641, 1006),]
day277 <- gopbubbleDF[c(277, 642, 1007),]
day278 <- gopbubbleDF[c(278, 643, 1008),]
day279 <- gopbubbleDF[c(279, 644, 1009),]
day280 <- gopbubbleDF[c(280, 645, 1010),]
day281 <- gopbubbleDF[c(281, 646, 1011),]
day282 <- gopbubbleDF[c(282, 647, 1012),]
day283 <- gopbubbleDF[c(283, 648, 1013),]
day284 <- gopbubbleDF[c(284, 649, 1014),]
day285 <- gopbubbleDF[c(285, 650, 1015),]
day286 <- gopbubbleDF[c(286, 651, 1016),]
day287 <- gopbubbleDF[c(287, 652, 1017),]
day288 <- gopbubbleDF[c(288, 653, 1018),]
day289 <- gopbubbleDF[c(289, 654, 1019),]
day290 <- gopbubbleDF[c(290, 655, 1020),]
day291 <- gopbubbleDF[c(291, 656, 1021),]
day292 <- gopbubbleDF[c(292, 657, 1022),]
day293 <- gopbubbleDF[c(293, 658, 1023),]
day294 <- gopbubbleDF[c(294, 659, 1024),]
day295 <- gopbubbleDF[c(295, 660, 1025),]
day296 <- gopbubbleDF[c(296, 661, 1026),]
day297 <- gopbubbleDF[c(297, 662, 1027),]
day298 <- gopbubbleDF[c(298, 663, 1028),]
day299 <- gopbubbleDF[c(299, 664, 1029),]
day300 <- gopbubbleDF[c(300, 665, 1030),]
day301 <- gopbubbleDF[c(301, 666, 1031),]
day302 <- gopbubbleDF[c(302, 667, 1032),]
day303 <- gopbubbleDF[c(303, 668, 1033),]
day304 <- gopbubbleDF[c(304, 669, 1034),]
day305 <- gopbubbleDF[c(305, 670, 1035),]
day306 <- gopbubbleDF[c(306, 671, 1036),]
day307 <- gopbubbleDF[c(307, 672, 1037),]
day308 <- gopbubbleDF[c(308, 673, 1038),]
day309 <- gopbubbleDF[c(309, 674, 1039),]
day310 <- gopbubbleDF[c(310, 675, 1040),]
day311 <- gopbubbleDF[c(311, 676, 1041),]
day312 <- gopbubbleDF[c(312, 677, 1042),]
day313 <- gopbubbleDF[c(313, 678, 1043),]
day314 <- gopbubbleDF[c(314, 679, 1044),]
day315 <- gopbubbleDF[c(315, 680, 1045),]
day316 <- gopbubbleDF[c(316, 681, 1046),]
day317 <- gopbubbleDF[c(317, 682, 1047),]
day318 <- gopbubbleDF[c(318, 683, 1048),]
day319 <- gopbubbleDF[c(319, 684, 1049),]
day320 <- gopbubbleDF[c(320, 685, 1050),]
day321 <- gopbubbleDF[c(321, 686, 1051),]
day322 <- gopbubbleDF[c(322, 687, 1052),]
day323 <- gopbubbleDF[c(323, 688, 1053),]
day324 <- gopbubbleDF[c(324, 689, 1054),]
day325 <- gopbubbleDF[c(325, 690, 1055),]
day326 <- gopbubbleDF[c(326, 691, 1056),]
day327 <- gopbubbleDF[c(327, 692, 1057),]
day328 <- gopbubbleDF[c(328, 693, 1058),]
day329 <- gopbubbleDF[c(329, 694, 1059),]
day330 <- gopbubbleDF[c(330, 695, 1060),]
day331 <- gopbubbleDF[c(331, 696, 1061),]
day332 <- gopbubbleDF[c(332, 697, 1062),]
day333 <- gopbubbleDF[c(333, 698, 1063),]
day334 <- gopbubbleDF[c(334, 699, 1064),]
day335 <- gopbubbleDF[c(335, 700, 1065),]
day336 <- gopbubbleDF[c(336, 701, 1066),]
day337 <- gopbubbleDF[c(337, 702, 1067),]
day338 <- gopbubbleDF[c(338, 703, 1068),]
day339 <- gopbubbleDF[c(339, 704, 1069),]
day340 <- gopbubbleDF[c(340, 705, 1070),]
day341 <- gopbubbleDF[c(341, 706, 1071),]
day342 <- gopbubbleDF[c(342, 707, 1072),]
day343 <- gopbubbleDF[c(343, 708, 1073),]
day344 <- gopbubbleDF[c(344, 709, 1074),]
day345 <- gopbubbleDF[c(345, 710, 1075),]
day346 <- gopbubbleDF[c(346, 711, 1076),]
day347 <- gopbubbleDF[c(347, 712, 1077),]
day348 <- gopbubbleDF[c(348, 713, 1078),]
day349 <- gopbubbleDF[c(349, 714, 1079),]
day350 <- gopbubbleDF[c(350, 715, 1080),]
day351 <- gopbubbleDF[c(351, 716, 1081),]
day352 <- gopbubbleDF[c(352, 717, 1082),]
day353 <- gopbubbleDF[c(353, 718, 1083),]
day354 <- gopbubbleDF[c(354, 719, 1084),]
day355 <- gopbubbleDF[c(355, 720, 1085),]
day356 <- gopbubbleDF[c(356, 721, 1086),]
day357 <- gopbubbleDF[c(357, 722, 1087),]
day358 <- gopbubbleDF[c(358, 723, 1088),]
day359 <- gopbubbleDF[c(359, 724, 1089),]
day360 <- gopbubbleDF[c(360, 725, 1090),]
day361 <- gopbubbleDF[c(361, 726, 1091),]
day362 <- gopbubbleDF[c(362, 727, 1092),]
day363 <- gopbubbleDF[c(363, 728, 1093),]
day364 <- gopbubbleDF[c(364, 729, 1094),]
day365 <- gopbubbleDF[c(365, 730, 1095),]


day1plot <- ggplot(day1, aes(x = Percentage, y = Change.In.Polling.Percentage, size = Searched, fill=Candidate, label=Candidate)) +
  labs(x="Total Percentage of GOP Vote", y="Change in Percentage")+
  geom_point(shape = 21, show_guide = FALSE) +
  geom_text(size=4)+
  # map z to area and make larger circles
  scale_size_area(max_size = 70) +
  scale_x_continuous(breaks = c(20, 40, 60, 80), limit = c(0, 100), 
                     expand = c(0, 0)) +
  scale_y_continuous(breaks = c(-40, -20, 0, 20, 40), limit = c(-60, 60), 
                     expand = c(0, 0)) +
  theme_bw() +
  theme(panel.grid.major = element_blank(),
        plot.title = element_text(size = rel(1.5), face = "bold", vjust = 1.5),
        axis.title=element_blank(),
        axis.text.x=element_text(size=10),
        axis.text.y=element_text(size=10))+

ggtitle(paste(as.character(day1$Date[1]), " GOP"))
ggsave(filename="GOPday1.png")
## Saving 7 x 5 in image

Creating the Videos

Now that I have two sets of 365 individual PNG image files representing each day of 2015, I needed a way to combine this. I imported the videos into my video editor, Final Cut, and set each image to show for 5 frames, then exported the video as an M4V video.

Using Final Cut Using Final Cut

If you want to recreate the process yourself, I have the zip files of all the PNG’s for each party saved as ZIP files, which can be downloaded here:

for the GOP: http://andrewshinsuke.me/cs125/GOP.zip for the Democrats: http://andrewshinsuke.me/cs125/Democrat.zip

Here are the videos Republican Party

Democratic Party

Conclusion

Although I could not make any definative judgments about how politicians can tie themselves to popular (or unpopular) causes, through these motion graphs, we can make some inferences.

First, we can observe Bernie Sanders rise to fame over Hillary Clinton. As he tied himself with issues popular with the younger generation such as free college tuition, Black Lives Matter, and his stance against the War on Drugs, you can see him becoming more and more popular in Google search indexes.

Contrasting that with Donald Trump, his often controversial comments have given him large notoriety on the internet, and can be attributed to his dominance in the GOP polls.

Although I have stopped here, I believe much more can be done with the data I have formed. For example, using the “related searches” data frame that gets returned in the gtrends function, someone could potentially create an algorithm that filters out the irrelevant searches, and searches for specific contexts the candidate was involved in within a given timespan. With that data, we would really be able to see if successful positioning as pro or con for a viral social media movement can affeect a politicians success.

At the same time, the three dimentional array I created from the Polling data from Huffington Post can be used in a study that compares different media outlets, and how their own perceived political leanings can have (or not have) an affect on their given poll ratings.